system

Abstract

The system according to this embodiment aims to provide immediate feedback and analyze mental state based on real-time health data. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, an image generation unit, and an NLP analysis unit. The data collection unit collects real-time data from a wearable device. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The feedback provision unit provides immediate feedback based on the health status evaluated by the analysis unit. The image generation unit visualizes the user's state after the plan is implemented based on the feedback provided by the feedback provision unit. The NLP analysis unit analyzes the user's mental state based on the image generated by the image generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, immediate feedback based on real-time health data and analysis of the user's mental state have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to perform immediate feedback based on real-time health data and analysis of the mental state.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, an image generation unit, and an NLP analysis unit. The data collection unit collects real-time data from a wearable device. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The feedback provision unit provides immediate feedback based on the health status evaluated by the analysis unit. The image generation unit visualizes the user's state after the plan is implemented based on the feedback provided by the feedback provision unit. The NLP analysis unit analyzes the user's mental state based on the image generated by the image generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide immediate feedback and analyze mental state based on real-time health data. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls 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), etc.

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The healthcare assistant system according to an embodiment of the present invention is a system that utilizes natural language processing and image generation AI to provide an optimal "personalized plan" to health-conscious individuals, wearable device users, and people aiming to improve their lifestyles. The healthcare assistant system collects real-time data from devices such as smartwatches, and the AI ​​analyzes that data. For example, data such as heart rate, steps taken, and sleep patterns are collected. Based on this data, the system evaluates the user's current health status and provides immediate feedback. For example, if the user is not getting enough exercise, a notification is sent to encourage exercise. Next, the system uses image generation AI to visualize what the user will look like after implementing the plan. For example, if a user implements a diet plan, the system predicts their body shape after four months and displays it as an image. This can increase the user's motivation to work towards their goal. Furthermore, the system utilizes NLP to analyze the user's mental state. For example, it analyzes text and voice data entered by the user to evaluate stress levels and emotional states. Based on this evaluation, it provides advice on stress reduction and improving mental health. For example, it suggests breathing exercises or meditation to help the user relax. The system also has a function to analyze photos of meals and calculate calories. For example, when a user takes a photo of a meal, the AI ​​analyzes the photo and calculates the calories consumed. This allows the user to check their calorie intake and balance, and maintain a healthy diet. Furthermore, medical supervision ensures a safe plan. For example, by providing a plan supervised by a doctor, the AI ​​can ensure that the user can implement the plan with peace of mind. Finally, as an incentive for continuation, points are awarded for continuing health management. For example, points are accumulated when a certain amount of exercise is achieved, and these points can be used to receive various benefits. In this way, the present invention utilizes natural language processing and image generation AI to provide users with an optimal health plan, and supports comprehensive healthcare by providing real-time feedback and mental health advice.This allows the healthcare assistant system to support comprehensive healthcare by evaluating the user's health status in real time and providing immediate feedback and analysis of their mental state.

[0029] The healthcare assistant system according to this embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, an image generation unit, and an NLP analysis unit. The data collection unit collects real-time data from wearable devices. The data collection unit can collect data such as heart rate, steps, and sleep patterns. For example, the data collection unit can acquire heart rate data from a smartwatch. The data collection unit can also collect steps data from a pedometer. Furthermore, the data collection unit can also collect sleep pattern data from a sleep tracker. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. For example, the analysis unit can analyze heart rate data to evaluate the user's cardiac health status. The analysis unit can also analyze steps data to evaluate the user's exercise level. Furthermore, the analysis unit can analyze sleep pattern data to evaluate the user's sleep quality. The feedback provision unit provides immediate feedback based on the health status evaluated by the analysis unit. For example, the feedback provision unit can provide notifications encouraging exercise in cases of insufficient exercise. The feedback unit can also provide notifications to encourage relaxation when the heart rate is high. Furthermore, the feedback unit can provide advice on improving sleep quality when sleep quality is poor. The image generation unit visualizes the user's appearance after implementing the plan based on the feedback provided by the feedback unit. For example, the image generation unit can predict and display an image of the user's body shape after implementing a diet plan. It can also predict and display an image of muscle development after implementing a strength training plan. Furthermore, it can predict and display an image of a relaxed state after implementing a stress reduction plan. The NLP analysis unit analyzes the user's mental state based on the images generated by the image generation unit. For example, the NLP analysis unit can analyze text and voice data entered by the user to evaluate stress levels and emotional states. It can also suggest breathing exercises and meditations to help the user relax based on their mental state.Furthermore, the NLP analysis unit can also provide advice for stress reduction based on the user's mental state. This allows the healthcare assistant system according to the embodiment to support comprehensive healthcare by evaluating the user's health status in real time and providing immediate feedback and analysis of their mental state.

[0030] The data collection unit collects real-time data from wearable devices. For example, it can collect data such as heart rate, steps, and sleep patterns. Specifically, when acquiring heart rate data from a smartwatch, it uses an optical heart rate sensor to detect changes in blood flow and measure this as heart rate. The smartwatch is worn on the user's wrist and can monitor heart rate 24 hours a day. Furthermore, the data collection unit can also collect step count data from a pedometer. The pedometer has a built-in accelerometer that detects the user's walking motion and counts steps. This allows for accurate tracking of the user's daily exercise. Additionally, the data collection unit can collect sleep pattern data from a sleep tracker. The sleep tracker monitors the user's movements, heart rate, and breathing patterns during sleep, and analyzes sleep quality and sleep cycles based on this data. This allows for detailed understanding of the user's sleep depth, frequency of interruptions, and the ratio of REM to non-REM sleep. The data collection unit centrally manages this data and transmits it to a central database in real time. Because the data is encrypted and privacy is thoroughly protected, users can use the device with peace of mind. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses tailored to the user's lifestyle and health condition. For example, data can be collected more frequently for users who are physically active, providing more detailed feedback. This allows the data collection unit to understand the user's health condition in real time and improve the overall performance of the system.

[0031] The analysis unit analyzes data collected by the data collection unit to assess the user's health status. For example, the analysis unit can analyze heart rate data to assess the user's cardiac health. Specifically, it analyzes heart rate data over time to evaluate fluctuations in resting heart rate and heart rate during exercise. This allows for early detection of abnormal increases or decreases in heart rate, enabling monitoring of cardiac health. The analysis unit can also analyze step count data to assess the user's exercise level. Step count data is aggregated daily, weekly, and monthly to understand the user's exercise habits and trends. Furthermore, the analysis unit can analyze sleep pattern data to assess the user's sleep quality. It analyzes sleep depth, frequency of interruptions, and the ratio of REM sleep to non-REM sleep to comprehensively assess the user's sleep quality. Based on this data, the analysis unit can comprehensively assess the user's health status and provide individualized health advice. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term health trends. For example, based on past heart rate data, it can evaluate fluctuations in cardiac health over a specific period and predict future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to handle not only real-time health assessment but also long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The feedback unit provides immediate feedback based on the health status evaluated by the analysis unit. For example, the feedback unit can provide notifications encouraging exercise if the user is not getting enough exercise. Specifically, it sends notifications to the user's smartphone or smartwatch, suggesting the importance of exercise and specific exercise plans. The feedback unit can also provide notifications encouraging relaxation if the user's heart rate is high. For example, it suggests deep breathing or meditation techniques to help the user relax. Furthermore, the feedback unit can provide advice on improving sleep quality if the user's sleep quality is poor. For example, it suggests pre-sleep routines, ways to create a suitable environment, and relaxation techniques to improve the user's sleep quality. The feedback unit supports users in leading a healthy life by providing personalized advice tailored to their health status. In addition, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice it provides. For example, it records how the user responded to the advice provided and incorporates this into future advice. The feedback unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the feedback unit to provide users with quick and reliable action instructions and support the improvement of their health.

[0033] The image generation unit visualizes the user's appearance after implementing a plan, based on feedback provided by the feedback unit. For example, the image generation unit can predict the user's body shape after implementing a diet plan and display it as an image. Specifically, based on the user's current body shape data, it simulates the weight loss and changes in body fat percentage that would occur if the diet plan were implemented, and displays this as a 3D model. The image generation unit can also predict muscle development after implementing a strength training plan and display it as an image. For example, it can simulate the increase in muscle mass after training a specific muscle group and visually show it to the user. Furthermore, the image generation unit can predict a relaxed appearance after implementing a stress reduction plan and display it as an image. For example, it can simulate a relaxed expression and posture after meditation or yoga and show it to the user. This allows the image generation unit to visually confirm the results that can be obtained by implementing the plan, thereby increasing motivation. In addition, the image generation unit can improve the accuracy and realism of the generated images based on user feedback. For example, it can collect feedback on the results of the user actually implementing the plan and reflect it in the next simulation. Furthermore, the image generation unit can use AI technology to simulate changes in the user's face and body shape in real time, generating more realistic images. This allows the image generation unit to show the user concrete and realistic results, thereby facilitating the implementation of the plan.

[0034] The NLP analysis unit analyzes the user's mental state based on images generated by the image generation unit. For example, the NLP analysis unit can analyze text and audio data entered by the user to evaluate stress levels and emotional states. Specifically, it analyzes text data entered by the user as a diary or memo using natural language processing techniques to evaluate the frequency of positive and negative emotions. It can also analyze audio data to infer emotional states from changes in voice tone and speaking style. Furthermore, the NLP analysis unit can suggest breathing techniques and meditation to promote relaxation based on the user's mental state. For example, if the user is stressed, it can suggest deep breathing or meditation techniques to encourage relaxation. The NLP analysis unit can also provide stress reduction advice based on the user's mental state. For example, it can identify the cause of stress and suggest specific ways to address it. In this way, the NLP analysis unit can comprehensively support the user's mental health, reducing stress and stabilizing emotions. Moreover, the NLP analysis unit can continuously improve the accuracy of its analysis algorithms and the effectiveness of its suggestions based on user feedback. For example, the system records how users react to the advice provided and incorporates this information into future suggestions. Furthermore, the NLP analysis unit uses AI technology to monitor the user's mental state in real time and detect anomalies early. This allows the NLP analysis unit to provide real-time support for the user's mental health and offer comprehensive healthcare.

[0035] The data collection unit can collect data such as heart rate, steps, and sleep patterns. For example, the data collection unit may use a smartwatch to collect heart rate data. For example, the data collection unit may use a pedometer to collect steps data. For example, the data collection unit may use a sleep tracker to collect sleep pattern data. This allows the data collection unit to collect data to gain a detailed understanding of the user's health status. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input heart rate data obtained from a smartwatch into an AI, which can then analyze the data to detect fluctuations in heart rate.

[0036] The feedback unit can provide notifications to encourage exercise when the user is not getting enough exercise. For example, the feedback unit can send a notification to encourage exercise if the user has not reached a certain number of steps. The feedback unit can also send a notification to encourage the user to stand up and walk if the user has been sitting for a long time. The feedback unit can also notify the user of the time to start exercising. In this way, the feedback unit can provide feedback to help the user overcome their lack of exercise. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's exercise data into AI, which can then detect the lack of exercise and generate a notification.

[0037] The image generation unit can predict the body shape after the plan is implemented and display it as an image. For example, the image generation unit can predict the body shape after implementing a diet plan and display it as an image. For example, the image generation unit can also predict muscle development after implementing a strength training plan and display it as an image. For example, the image generation unit can predict a relaxed posture after implementing a stress reduction plan and display it as an image. In this way, the image generation unit can increase the user's motivation to work towards their goals. Some or all of the above processing in the image generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the image generation unit can input the user's current body shape data into a generation AI, and the generation AI can predict the body shape after the plan is implemented and generate an image.

[0038] The NLP analysis unit can analyze text and audio data entered by the user and evaluate their stress level and emotional state. For example, the NLP analysis unit can analyze text data entered by the user and evaluate their stress level. The NLP analysis unit can also analyze audio data entered by the user and evaluate their emotional state. For example, the NLP analysis unit can combine and analyze the user's text and audio data to evaluate their overall mental state. This allows the NLP analysis unit to analyze the user's mental state and provide appropriate advice. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's text data into an AI, which can then evaluate the stress level and generate advice.

[0039] The NLP analysis unit can suggest breathing techniques and meditation for relaxation. For example, the NLP analysis unit may suggest breathing techniques for relaxation if the user's stress level is high. The NLP analysis unit may also suggest meditation if the user's emotional state is unstable. The NLP analysis unit may also suggest music or ambient sounds for relaxation based on the user's mental state. In this way, the NLP analysis unit can support the improvement of the user's mental health. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's emotional state data into AI, and the AI ​​can generate suggestions for breathing techniques and meditation for relaxation.

[0040] A meal analysis unit analyzes photos of meals and calculates calories. The meal analysis unit can analyze photos of meals and calculate calories. For example, the meal analysis unit can analyze photos of meals taken by the user using image recognition technology and calculate the calories consumed. The meal analysis unit can also identify the types of food from the photos of meals and sum up the calories of each. The meal analysis unit can also evaluate the balance of nutrients from the photos of meals and provide advice for maintaining a healthy diet. This allows the meal analysis unit to help users understand their calorie intake and maintain a healthy diet. Some or all of the above processing in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input photos of meals taken by the user into an AI, which can then analyze the images and calculate the calories.

[0041] A medical supervision department provides plans supervised by physicians. The medical supervision department can provide plans supervised by physicians. For example, the medical supervision department can provide a meal plan supervised by a physician. The medical supervision department can also provide an exercise plan supervised by a physician. The medical supervision department can also provide a mental health plan supervised by a physician. This allows the medical supervision department to ensure that users can implement the plan with confidence. Some or all of the above processes in the medical supervision department may be performed using AI or not. For example, the medical supervision department can input a physician-supervised plan into an AI, which can then provide the user with the most suitable plan.

[0042] An incentive provision unit that awards points for continuing health management. The incentive provision unit can award points for continuing health management. For example, the incentive provision unit may award points when a user achieves a certain amount of exercise. For example, the incentive provision unit may also award points when a user continues to eat a healthy diet. For example, the incentive provision unit may also award points when a user implements a mental health plan. This allows the incentive provision unit to increase the user's motivation to continue health management. Some or all of the above processing in the incentive provision unit may be performed using AI or not. For example, the incentive provision unit may input the user's health management data into AI, and the AI ​​may calculate and award points.

[0043] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and collect heart rate data at the optimal time. For example, the data collection unit can analyze the user's past step count data and adjust the frequency of step count collection. For example, the data collection unit can analyze the user's past sleep patterns and optimize the method of collecting sleep data. This allows the data collection unit to select the optimal data collection method based on past data and improve accuracy. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past health data into AI, which can then select the optimal data collection method.

[0044] The data collection unit can dynamically change the types of data it collects based on the user's current activity level during data collection. For example, if the user is exercising, the data collection unit will prioritize collecting heart rate and step count data. If the user is resting, the data collection unit can also collect sleep data and stress level data. If the user is eating, the data collection unit can also collect photos of the meal and calorie intake data. This allows the data collection unit to efficiently collect data by changing the types of data it collects according to the user's activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user activity data into the AI, and the types of data the AI ​​collects can be dynamically changed.

[0045] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a park, the data collection unit can prioritize the collection of exercise data and heart rate. For example, if the user is at home, the data collection unit can prioritize the collection of sleep data and stress levels. For example, if the user is in a restaurant, the data collection unit can prioritize the collection of food photos and calorie intake data. This allows the data collection unit to collect highly relevant data based on the user's location information and improve accuracy. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's location information data into AI, which can then prioritize the collection of highly relevant data.

[0046] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. For example, if the user posts about food, the data collection unit can also collect photos of meals and calorie intake data. For example, if the user posts about stress, the data collection unit can also collect stress level data. This allows the data collection unit to collect relevant data based on the user's social media activity and improve accuracy. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into AI, and the AI ​​can collect relevant data.

[0047] The analysis unit can improve the accuracy of its analysis by referring to past health data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data to improve the accuracy of its current heart rate analysis. The analysis unit can also refer to the user's past step count data to improve the accuracy of its current step count analysis. The analysis unit can also refer to the user's past sleep patterns to improve the accuracy of its current sleep data analysis. In this way, the analysis unit can improve the accuracy of its analysis based on past data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past health data into an AI, which can then analyze the data to improve accuracy.

[0048] The analysis unit can apply different analysis methods based on the user's lifestyle during analysis. For example, if the user has an exercise habit, the analysis unit will apply an analysis method for exercise data. For example, if the user is mindful of their diet, the analysis unit may also apply an analysis method for diet data. For example, if the user prioritizes stress management, the analysis unit may also apply an analysis method for stress data. This allows the analysis unit to apply analysis methods according to the user's lifestyle and improve the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's lifestyle data into the AI, which can then select and apply an appropriate analysis method.

[0049] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, the analysis unit can adjust the analysis of heart rate data if the user is at high altitude. For example, the analysis unit can also adjust the analysis of step count data if the user is in an urban area. For example, the analysis unit can adjust the analysis of sleep data if the user is at home. This allows the analysis unit to perform analysis based on the user's location information and improve accuracy. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's location information data into AI, and the AI ​​can analyze the data to improve accuracy.

[0050] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers on the user's health status. The analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the user's lifestyle. The analysis unit can also improve the accuracy of its analysis by referring to literature on the user's mental health. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's relevant literature data into AI, and the AI ​​can analyze the literature to improve accuracy.

[0051] The feedback provider can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback provider can refer to feedback the user has received in the past and provide optimal feedback in similar situations. For example, the feedback provider can select effective feedback from the user's past feedback history. For example, the feedback provider can analyze the user's past feedback history and provide the most appropriate feedback. In this way, the feedback provider can provide optimal feedback based on past feedback history and enhance its effectiveness. Some or all of the above processes in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's past feedback history data into AI, and the AI ​​can select and provide optimal feedback.

[0052] The feedback provider can adjust the frequency of feedback based on the user's current health status when providing feedback. For example, if the user's health is good, the feedback provider can reduce the frequency of feedback. For example, if the user's health is deteriorating, the feedback provider can also increase the frequency of feedback. For example, if the user is trying to achieve a specific health goal, the feedback provider can adjust the frequency of feedback to match that goal. This allows the feedback provider to adjust the frequency of feedback according to the user's health status and provide feedback at the appropriate time. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's health status data into the AI, which can then adjust the frequency of feedback.

[0053] The feedback provider can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, if the user is in a park, the feedback provider can provide feedback related to exercise. For example, if the user is at home, the feedback provider can also provide feedback related to relaxation. For example, if the user is in a restaurant, the feedback provider can also provide feedback related to food. In this way, the feedback provider can provide optimal feedback based on the user's location information and enhance its effectiveness. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's location data into AI, and the AI ​​can select and provide the optimal feedback.

[0054] The feedback provider can analyze the user's social media activity and adjust the content of the feedback when providing it. For example, if the user posts about exercise on social media, the feedback provider can provide feedback about exercise. For example, if the user posts about food, the feedback provider can also provide feedback about food. For example, if the user posts about stress, the feedback provider can also provide feedback on stress reduction. In this way, the feedback provider can adjust the content of the feedback based on the user's social media activity and enhance its effectiveness. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's social media data into AI, and the AI ​​can adjust the content of the feedback.

[0055] The image generation unit can improve the accuracy of image generation by referring to the user's past health data during image generation. For example, the image generation unit can refer to the user's past weight data to accurately predict the body shape after implementing a diet plan. For example, the image generation unit can also refer to the user's past exercise data to accurately predict muscle development. For example, the image generation unit can refer to the user's past diet data to accurately predict the health status after improving dietary habits. This allows the image generation unit to improve the accuracy of image generation based on past health data. Some or all of the above-described processes in the image generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the image generation unit can input the user's past health data into a generation AI, which can then analyze the data to improve the accuracy of image generation.

[0056] The image generation unit can apply different generation methods based on the user's lifestyle habits when generating images. For example, if the user has an exercise habit, the image generation unit can generate an image that emphasizes the user's physique after exercise. For example, if the user is mindful of their diet, the image generation unit can also generate an image that emphasizes their health after improving their diet. For example, if the user prioritizes stress management, the image generation unit can also generate an image that emphasizes a relaxed state. In this way, the image generation unit can apply a generation method according to the user's lifestyle habits and improve generation accuracy. Some or all of the above-described processes in the image generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the image generation unit can input the user's lifestyle data into a generation AI, which can then select and apply an appropriate generation method.

[0057] The image generation unit can generate the optimal image by considering the user's geographical location information during image generation. For example, if the user is at the beach, the image generation unit can generate an image with a beach landscape as the background. For example, if the user is in a mountainous area, the image generation unit can also generate an image with a mountain landscape as the background. For example, if the user is in an urban area, the image generation unit can also generate an image with an urban landscape as the background. In this way, the image generation unit can generate the optimal image based on the user's location information and enhance the visual effect. Some or all of the above processing in the image generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the image generation unit can input the user's location information data into a generation AI, and the generation AI can generate the optimal image.

[0058] The image generation unit can analyze the user's social media activity and adjust the content of the generated image during the generation process. For example, if the user posts about exercise on social media, the image generation unit can generate an image that emphasizes the user's physique after exercise. For example, if the user posts about food, the image generation unit can also generate an image that emphasizes the user's health after improving their diet. For example, if the user posts about stress, the image generation unit can also generate an image that emphasizes a relaxed state. In this way, the image generation unit can adjust the content of the generated image based on the user's social media activity and enhance its visual effect. Some or all of the above-described processes in the image generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the image generation unit can input the user's social media data into a generation AI, which can then adjust the content of the generated image.

[0059] The NLP analysis unit can improve the accuracy of its analysis by referring to the user's past text and voice data during NLP analysis. For example, the NLP analysis unit can refer to the user's past text data to improve the accuracy of its analysis of the user's current emotional state. The NLP analysis unit can also refer to the user's past voice data to improve the accuracy of its analysis of the user's current stress level. The NLP analysis unit can also refer to the user's past message history to analyze changes in emotional state. This allows the NLP analysis unit to improve the accuracy of its analysis based on past data. Some or all of the above processes in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's past text and voice data into an AI, which can then analyze the data to improve accuracy.

[0060] The NLP analysis unit can apply different analysis methods based on the user's lifestyle habits during NLP analysis. For example, if the user has an exercise habit, the NLP analysis unit can apply analysis methods related to exercise-related text and audio data. For example, if the user is mindful of their diet, the NLP analysis unit can also apply analysis methods related to diet-related text and audio data. For example, if the user prioritizes stress management, the NLP analysis unit can also apply analysis methods related to stress-related text and audio data. This allows the NLP analysis unit to apply analysis methods according to the user's lifestyle habits and improve analysis accuracy. Some or all of the above-described processes in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's lifestyle data into AI, which can then select and apply an appropriate analysis method.

[0061] The NLP analysis unit can perform NLP analysis while taking into account the user's geographical location information. For example, if the user is at high altitude, the NLP analysis unit can adjust the analysis of heart rate data. For example, if the user is in an urban area, the NLP analysis unit can also adjust the analysis of step count data. For example, if the user is at home, the NLP analysis unit can also adjust the analysis of sleep data. This allows the NLP analysis unit to perform analysis based on the user's location information and improve accuracy. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's location information data into AI, and the AI ​​can analyze the data to improve accuracy.

[0062] The NLP analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during NLP analysis. For example, the NLP analysis unit can improve the accuracy of its analysis by referring to the latest research papers on the user's health status. The NLP analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the user's lifestyle habits. The NLP analysis unit can also improve the accuracy of its analysis by referring to literature on the user's mental health. In this way, the NLP analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's relevant literature data into AI, and the AI ​​can analyze the literature to improve accuracy.

[0063] The meal analysis unit can improve the accuracy of its analysis by referring to the user's past meal data during meal analysis. For example, the meal analysis unit can refer to the user's past meal data to improve the accuracy of its analysis of the current meal. For example, the meal analysis unit can also refer to the user's past calorie data to improve the accuracy of its analysis of the current calorie intake. For example, the meal analysis unit can refer to the user's past nutrition data to improve the accuracy of its analysis of the current nutritional balance. In this way, the meal analysis unit can improve the accuracy of its analysis based on past data. Some or all of the above processes in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input the user's past meal data into AI, and the AI ​​can analyze the data to improve accuracy.

[0064] The diet analysis unit can apply different analysis methods based on the user's lifestyle during diet analysis. For example, if the user has an exercise habit, the diet analysis unit can apply an analysis method related to exercise-related dietary data. For example, if the user is mindful of their diet, the diet analysis unit can also apply an analysis method related to diet-related data. For example, if the user prioritizes stress management, the diet analysis unit can also apply an analysis method related to stress-related dietary data. This allows the diet analysis unit to apply analysis methods according to the user's lifestyle and improve analysis accuracy. Some or all of the above processing in the diet analysis unit may be performed using AI or not. For example, the diet analysis unit can input the user's lifestyle data into AI, which can then select and apply an appropriate analysis method.

[0065] The meal analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user is at high altitude, the meal analysis unit can adjust the analysis of the nutritional balance of the meal. For example, if the user is in an urban area, the meal analysis unit can also adjust the analysis of the calorie intake of the meal. For example, if the user is at home, the meal analysis unit can also adjust the analysis of the nutritional balance of the meal. This allows the meal analysis unit to perform analysis based on the user's location information and improve accuracy. Some or all of the above processing in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input the user's location information data into AI, and the AI ​​can analyze the data to improve accuracy.

[0066] The diet analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during the analysis. For example, the diet analysis unit can improve the accuracy of its analysis by referring to the latest research papers on the user's diet. The diet analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the user's nutritional balance. The diet analysis unit can also improve the accuracy of its analysis by referring to literature on the user's calorie intake. In this way, the diet analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the diet analysis unit may be performed using AI or not. For example, the diet analysis unit can input the user's relevant literature data into the AI, and the AI ​​can analyze the literature to improve accuracy.

[0067] The medical supervision department can improve the accuracy of medical supervision by referring to the user's past medical data during medical supervision. For example, the medical supervision department can refer to the user's past medical records to improve the accuracy of current medical supervision. The medical supervision department can also refer to the user's past health checkup data to improve the accuracy of current medical supervision. The medical supervision department can also refer to the user's past prescription history to improve the accuracy of current medical supervision. In this way, the medical supervision department can improve the accuracy of supervision based on past medical data. Some or all of the above processes in the medical supervision department may be performed using AI or not. For example, the medical supervision department can input the user's past medical data into AI, and the AI ​​can analyze the data to improve accuracy.

[0068] The Medical Supervision Department can apply different supervision methods based on the user's lifestyle during medical supervision. For example, if the user has an exercise habit, the Medical Supervision Department can apply exercise-related medical supervision methods. For example, if the user is mindful of their diet, the Medical Supervision Department can also apply diet-related medical supervision methods. For example, if the user places importance on stress management, the Medical Supervision Department can also apply stress-related medical supervision methods. This allows the Medical Supervision Department to apply supervision methods according to the user's lifestyle and improve the accuracy of supervision. Some or all of the above processes in the Medical Supervision Department may be performed using AI or not. For example, the Medical Supervision Department can input the user's lifestyle data into AI, which can then select and apply an appropriate supervision method.

[0069] The Medical Supervision Department can provide optimal supervision during medical supervision by taking into account the user's geographical location. For example, if the user is at high altitude, the Medical Supervision Department can provide medical supervision appropriate for high altitude. For example, if the user is in an urban area, the Medical Supervision Department can also provide medical supervision appropriate for urban areas. For example, if the user is at home, the Medical Supervision Department can also provide medical supervision appropriate for home use. This allows the Medical Supervision Department to provide optimal supervision based on the user's location information and enhance its effectiveness. Some or all of the above processing in the Medical Supervision Department may be performed using AI or not. For example, the Medical Supervision Department can input the user's location data into AI, and the AI ​​can analyze the data to provide optimal supervision.

[0070] The medical supervision department can improve the accuracy of its supervision by referring to relevant literature on the user during medical supervision. For example, the medical supervision department can improve the accuracy of its supervision by referring to the latest research papers on the user's health status. The medical supervision department can also improve the accuracy of its supervision by referring to relevant literature on the user's lifestyle. The medical supervision department can also improve the accuracy of its supervision by referring to literature on the user's mental health. In this way, the medical supervision department can improve the accuracy of its supervision by referring to relevant literature. Some or all of the above processes in the medical supervision department may be performed using AI or not. For example, the medical supervision department can input the user's relevant literature data into AI, and the AI ​​can analyze the literature to improve accuracy.

[0071] The incentive provision unit can provide the most suitable incentive by referring to the user's past incentive history when providing incentives. For example, the incentive provision unit can refer to incentives the user has received in the past and provide the most suitable incentive in a similar situation. For example, the incentive provision unit can select an effective incentive from the user's past incentive history. For example, the incentive provision unit can analyze the user's past incentive history and provide the most appropriate incentive. In this way, the incentive provision unit can provide the most suitable incentive based on past incentive history and enhance its effectiveness. Some or all of the above processes in the incentive provision unit may be performed using AI or not. For example, the incentive provision unit can input the user's past incentive history data into AI, and the AI ​​can select and provide the most suitable incentive.

[0072] The incentive provider can adjust the frequency of incentives based on the user's current health status when providing incentives. For example, if the user's health is good, the incentive provider can reduce the frequency of incentives. For example, if the user's health is deteriorating, the incentive provider can also increase the frequency of incentives. For example, if the user is trying to achieve a specific health goal, the incentive provider can adjust the frequency of incentives to match that goal. This allows the incentive provider to adjust the frequency of incentives according to the user's health status and provide incentives at the appropriate time. Some or all of the above processing in the incentive provider may be performed using AI or not. For example, the incentive provider can input user health data into AI, and the AI ​​can adjust the frequency of incentives.

[0073] The incentive provider can provide the most suitable incentives by considering the user's geographical location when providing incentives. For example, if the user is in a park, the incentive provider can provide incentives related to exercise. For example, if the user is at home, the incentive provider can also provide incentives for relaxation. For example, if the user is in a restaurant, the incentive provider can also provide incentives related to dining. This allows the incentive provider to provide the most suitable incentives based on the user's location information, thereby enhancing their effectiveness. Some or all of the above processing in the incentive provider may be performed using AI or not. For example, the incentive provider can input the user's location data into AI, which can then provide the most suitable incentives.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The healthcare assistant system can analyze a user's past health data and select the optimal data collection method. For example, it can analyze a user's past heart rate data and collect heart rate data at the optimal time. It can also analyze a user's past step count data and adjust the frequency of step count collection. Furthermore, it can analyze a user's past sleep patterns and optimize the sleep data collection method. As a result, the data collection unit can select the optimal data collection method based on past data and improve accuracy.

[0076] The healthcare assistant system can prioritize the collection of relevant data by considering the user's geographical location. For example, if the user is in a park, it can prioritize the collection of exercise data and heart rate. If the user is at home, it can prioritize the collection of sleep data and stress levels. Furthermore, if the user is in a restaurant, it can prioritize the collection of food photos and calorie intake data. This allows the data collection unit to collect more relevant data based on the user's location, improving accuracy.

[0077] The healthcare assistant system can apply different analysis methods based on the user's lifestyle during analysis. For example, if the user has an exercise habit, an exercise data analysis method can be applied. Similarly, if the user is mindful of their diet, a diet data analysis method can be applied. Furthermore, if the user prioritizes stress management, a stress data analysis method can be applied. This allows the analysis unit to apply the appropriate analysis method according to the user's lifestyle, thereby improving the accuracy of the analysis.

[0078] A healthcare assistant system can provide optimal feedback by referring to a user's past feedback history. For example, it can refer to feedback a user has received in the past and provide the most appropriate feedback in similar situations. It can also select effective feedback from a user's past feedback history. Furthermore, it can analyze a user's past feedback history and provide the most appropriate feedback. As a result, the feedback delivery unit can provide optimal feedback based on past feedback history, thereby enhancing its effectiveness.

[0079] The healthcare assistant system can provide the most suitable incentives by referring to the user's past incentive history. For example, it can refer to incentives the user has received in the past and provide the most suitable incentive in a similar situation. It can also select effective incentives from the user's past incentive history. Furthermore, it can analyze the user's past incentive history and provide the most appropriate incentive. As a result, the incentive provision unit can provide the most suitable incentives based on past incentive history, thereby increasing effectiveness.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The data collection unit collects real-time data from wearable devices. The data collection unit can collect data such as heart rate, steps, and sleep patterns. For example, the data collection unit can acquire heart rate data from a smartwatch. It can also collect step count data from a pedometer. Furthermore, it can collect sleep pattern data from a sleep tracker. Step 2: The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. For example, the analysis unit can analyze heart rate data to evaluate the user's cardiac health. It can also analyze step count data to evaluate the user's exercise level. Furthermore, the analysis unit can analyze sleep pattern data to evaluate the user's sleep quality. Step 3: The feedback unit provides immediate feedback based on the health status assessed by the analysis unit. For example, the feedback unit can provide notifications encouraging exercise if the user is not getting enough exercise. It can also provide notifications encouraging relaxation if the user has a high heart rate. Furthermore, it can provide advice on improving sleep if the user has poor sleep quality. Step 4: The image generation unit visualizes the post-plan implementation state based on the feedback provided by the feedback provision unit. For example, the image generation unit can predict the body shape after implementing a diet plan and display it as an image. It can also predict muscle development after implementing a strength training plan and display it as an image. Furthermore, it can predict a relaxed state after implementing a stress reduction plan and display it as an image. Step 5: The NLP analysis unit analyzes the user's mental state based on the images generated by the image generation unit. For example, the NLP analysis unit can analyze text and voice data entered by the user to evaluate stress levels and emotional states. The NLP analysis unit can also suggest breathing exercises or meditation techniques to help the user relax based on their mental state. Furthermore, the NLP analysis unit can provide advice for stress reduction based on the user's mental state.

[0082] (Example of form 2) The healthcare assistant system according to an embodiment of the present invention is a system that utilizes natural language processing and image generation AI to provide an optimal "personalized plan" to health-conscious individuals, wearable device users, and people aiming to improve their lifestyles. The healthcare assistant system collects real-time data from devices such as smartwatches, and the AI ​​analyzes that data. For example, data such as heart rate, steps taken, and sleep patterns are collected. Based on this data, the system evaluates the user's current health status and provides immediate feedback. For example, if the user is not getting enough exercise, a notification is sent to encourage exercise. Next, the system uses image generation AI to visualize what the user will look like after implementing the plan. For example, if a user implements a diet plan, the system predicts their body shape after four months and displays it as an image. This can increase the user's motivation to work towards their goal. Furthermore, the system utilizes NLP to analyze the user's mental state. For example, it analyzes text and voice data entered by the user to evaluate stress levels and emotional states. Based on this evaluation, it provides advice on stress reduction and improving mental health. For example, it suggests breathing exercises or meditation to help the user relax. The system also has a function to analyze photos of meals and calculate calories. For example, when a user takes a photo of a meal, the AI ​​analyzes the photo and calculates the calories consumed. This allows the user to check their calorie intake and balance, and maintain a healthy diet. Furthermore, medical supervision ensures a safe plan. For example, by providing a plan supervised by a doctor, the AI ​​can ensure that the user can implement the plan with peace of mind. Finally, as an incentive for continuation, points are awarded for continuing health management. For example, points are accumulated when a certain amount of exercise is achieved, and these points can be used to receive various benefits. In this way, the present invention utilizes natural language processing and image generation AI to provide users with an optimal health plan, and supports comprehensive healthcare by providing real-time feedback and mental health advice.This allows the healthcare assistant system to support comprehensive healthcare by evaluating the user's health status in real time and providing immediate feedback and analysis of their mental state.

[0083] The healthcare assistant system according to this embodiment comprises a data collection unit, an analysis unit, a feedback provision unit, an image generation unit, and an NLP analysis unit. The data collection unit collects real-time data from wearable devices. The data collection unit can collect data such as heart rate, steps, and sleep patterns. For example, the data collection unit can acquire heart rate data from a smartwatch. The data collection unit can also collect steps data from a pedometer. Furthermore, the data collection unit can also collect sleep pattern data from a sleep tracker. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. For example, the analysis unit can analyze heart rate data to evaluate the user's cardiac health status. The analysis unit can also analyze steps data to evaluate the user's exercise level. Furthermore, the analysis unit can analyze sleep pattern data to evaluate the user's sleep quality. The feedback provision unit provides immediate feedback based on the health status evaluated by the analysis unit. For example, the feedback provision unit can provide notifications encouraging exercise in cases of insufficient exercise. The feedback unit can also provide notifications to encourage relaxation when the heart rate is high. Furthermore, the feedback unit can provide advice on improving sleep quality when sleep quality is poor. The image generation unit visualizes the user's appearance after implementing the plan based on the feedback provided by the feedback unit. For example, the image generation unit can predict and display an image of the user's body shape after implementing a diet plan. It can also predict and display an image of muscle development after implementing a strength training plan. Furthermore, it can predict and display an image of a relaxed state after implementing a stress reduction plan. The NLP analysis unit analyzes the user's mental state based on the images generated by the image generation unit. For example, the NLP analysis unit can analyze text and voice data entered by the user to evaluate stress levels and emotional states. It can also suggest breathing exercises and meditations to help the user relax based on their mental state.Furthermore, the NLP analysis unit can also provide advice for stress reduction based on the user's mental state. This allows the healthcare assistant system according to the embodiment to support comprehensive healthcare by evaluating the user's health status in real time and providing immediate feedback and analysis of their mental state.

[0084] The data collection unit collects real-time data from wearable devices. For example, it can collect data such as heart rate, steps, and sleep patterns. Specifically, when acquiring heart rate data from a smartwatch, it uses an optical heart rate sensor to detect changes in blood flow and measure this as heart rate. The smartwatch is worn on the user's wrist and can monitor heart rate 24 hours a day. Furthermore, the data collection unit can also collect step count data from a pedometer. The pedometer has a built-in accelerometer that detects the user's walking motion and counts steps. This allows for accurate tracking of the user's daily exercise. Additionally, the data collection unit can collect sleep pattern data from a sleep tracker. The sleep tracker monitors the user's movements, heart rate, and breathing patterns during sleep, and analyzes sleep quality and sleep cycles based on this data. This allows for detailed understanding of the user's sleep depth, frequency of interruptions, and the ratio of REM to non-REM sleep. The data collection unit centrally manages this data and transmits it to a central database in real time. Because the data is encrypted and privacy is thoroughly protected, users can use the device with peace of mind. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses tailored to the user's lifestyle and health condition. For example, data can be collected more frequently for users who are physically active, providing more detailed feedback. This allows the data collection unit to understand the user's health condition in real time and improve the overall performance of the system.

[0085] The analysis unit analyzes data collected by the data collection unit to assess the user's health status. For example, the analysis unit can analyze heart rate data to assess the user's cardiac health. Specifically, it analyzes heart rate data over time to evaluate fluctuations in resting heart rate and heart rate during exercise. This allows for early detection of abnormal increases or decreases in heart rate, enabling monitoring of cardiac health. The analysis unit can also analyze step count data to assess the user's exercise level. Step count data is aggregated daily, weekly, and monthly to understand the user's exercise habits and trends. Furthermore, the analysis unit can analyze sleep pattern data to assess the user's sleep quality. It analyzes sleep depth, frequency of interruptions, and the ratio of REM sleep to non-REM sleep to comprehensively assess the user's sleep quality. Based on this data, the analysis unit can comprehensively assess the user's health status and provide individualized health advice. In addition, the analysis unit can utilize historical data and statistical information to analyze long-term health trends. For example, based on past heart rate data, it can evaluate fluctuations in cardiac health over a specific period and predict future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to handle not only real-time health assessment but also long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0086] The feedback unit provides immediate feedback based on the health status evaluated by the analysis unit. For example, the feedback unit can provide notifications encouraging exercise if the user is not getting enough exercise. Specifically, it sends notifications to the user's smartphone or smartwatch, suggesting the importance of exercise and specific exercise plans. The feedback unit can also provide notifications encouraging relaxation if the user's heart rate is high. For example, it suggests deep breathing or meditation techniques to help the user relax. Furthermore, the feedback unit can provide advice on improving sleep quality if the user's sleep quality is poor. For example, it suggests pre-sleep routines, ways to create a suitable environment, and relaxation techniques to improve the user's sleep quality. The feedback unit supports users in leading a healthy life by providing personalized advice tailored to their health status. In addition, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice it provides. For example, it records how the user responded to the advice provided and incorporates this into future advice. The feedback unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the feedback unit to provide users with quick and reliable action instructions and support the improvement of their health.

[0087] The image generation unit visualizes the user's appearance after implementing a plan, based on feedback provided by the feedback unit. For example, the image generation unit can predict the user's body shape after implementing a diet plan and display it as an image. Specifically, based on the user's current body shape data, it simulates the weight loss and changes in body fat percentage that would occur if the diet plan were implemented, and displays this as a 3D model. The image generation unit can also predict muscle development after implementing a strength training plan and display it as an image. For example, it can simulate the increase in muscle mass after training a specific muscle group and visually show it to the user. Furthermore, the image generation unit can predict a relaxed appearance after implementing a stress reduction plan and display it as an image. For example, it can simulate a relaxed expression and posture after meditation or yoga and show it to the user. This allows the image generation unit to visually confirm the results that can be obtained by implementing the plan, thereby increasing motivation. In addition, the image generation unit can improve the accuracy and realism of the generated images based on user feedback. For example, it can collect feedback on the results of the user actually implementing the plan and reflect it in the next simulation. Furthermore, the image generation unit can use AI technology to simulate changes in the user's face and body shape in real time, generating more realistic images. This allows the image generation unit to show the user concrete and realistic results, thereby facilitating the implementation of the plan.

[0088] The NLP analysis unit analyzes the user's mental state based on images generated by the image generation unit. For example, the NLP analysis unit can analyze text and audio data entered by the user to evaluate stress levels and emotional states. Specifically, it analyzes text data entered by the user as a diary or memo using natural language processing techniques to evaluate the frequency of positive and negative emotions. It can also analyze audio data to infer emotional states from changes in voice tone and speaking style. Furthermore, the NLP analysis unit can suggest breathing techniques and meditation to promote relaxation based on the user's mental state. For example, if the user is stressed, it can suggest deep breathing or meditation techniques to encourage relaxation. The NLP analysis unit can also provide stress reduction advice based on the user's mental state. For example, it can identify the cause of stress and suggest specific ways to address it. In this way, the NLP analysis unit can comprehensively support the user's mental health, reducing stress and stabilizing emotions. Moreover, the NLP analysis unit can continuously improve the accuracy of its analysis algorithms and the effectiveness of its suggestions based on user feedback. For example, the system records how users react to the advice provided and incorporates this information into future suggestions. Furthermore, the NLP analysis unit uses AI technology to monitor the user's mental state in real time and detect anomalies early. This allows the NLP analysis unit to provide real-time support for the user's mental health and offer comprehensive healthcare.

[0089] The data collection unit can collect data such as heart rate, steps, and sleep patterns. For example, the data collection unit may use a smartwatch to collect heart rate data. For example, the data collection unit may use a pedometer to collect steps data. For example, the data collection unit may use a sleep tracker to collect sleep pattern data. This allows the data collection unit to collect data to gain a detailed understanding of the user's health status. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input heart rate data obtained from a smartwatch into an AI, which can then analyze the data to detect fluctuations in heart rate.

[0090] The feedback unit can provide notifications to encourage exercise when the user is not getting enough exercise. For example, the feedback unit can send a notification to encourage exercise if the user has not reached a certain number of steps. The feedback unit can also send a notification to encourage the user to stand up and walk if the user has been sitting for a long time. The feedback unit can also notify the user of the time to start exercising. In this way, the feedback unit can provide feedback to help the user overcome their lack of exercise. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's exercise data into AI, which can then detect the lack of exercise and generate a notification.

[0091] The image generation unit can predict the body shape after the plan is implemented and display it as an image. For example, the image generation unit can predict the body shape after implementing a diet plan and display it as an image. For example, the image generation unit can also predict muscle development after implementing a strength training plan and display it as an image. For example, the image generation unit can predict a relaxed posture after implementing a stress reduction plan and display it as an image. In this way, the image generation unit can increase the user's motivation to work towards their goals. Some or all of the above processing in the image generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the image generation unit can input the user's current body shape data into a generation AI, and the generation AI can predict the body shape after the plan is implemented and generate an image.

[0092] The NLP analysis unit can analyze text and audio data entered by the user and evaluate their stress level and emotional state. For example, the NLP analysis unit can analyze text data entered by the user and evaluate their stress level. The NLP analysis unit can also analyze audio data entered by the user and evaluate their emotional state. For example, the NLP analysis unit can combine and analyze the user's text and audio data to evaluate their overall mental state. This allows the NLP analysis unit to analyze the user's mental state and provide appropriate advice. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's text data into an AI, which can then evaluate the stress level and generate advice.

[0093] The NLP analysis unit can suggest breathing techniques and meditation for relaxation. For example, the NLP analysis unit may suggest breathing techniques for relaxation if the user's stress level is high. The NLP analysis unit may also suggest meditation if the user's emotional state is unstable. The NLP analysis unit may also suggest music or ambient sounds for relaxation based on the user's mental state. In this way, the NLP analysis unit can support the improvement of the user's mental health. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's emotional state data into AI, and the AI ​​can generate suggestions for breathing techniques and meditation for relaxation.

[0094] A meal analysis unit analyzes photos of meals and calculates calories. The meal analysis unit can analyze photos of meals and calculate calories. For example, the meal analysis unit can analyze photos of meals taken by the user using image recognition technology and calculate the calories consumed. The meal analysis unit can also identify the types of food from the photos of meals and sum up the calories of each. The meal analysis unit can also evaluate the balance of nutrients from the photos of meals and provide advice for maintaining a healthy diet. This allows the meal analysis unit to help users understand their calorie intake and maintain a healthy diet. Some or all of the above processing in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input photos of meals taken by the user into an AI, which can then analyze the images and calculate the calories.

[0095] A medical supervision department provides plans supervised by physicians. The medical supervision department can provide plans supervised by physicians. For example, the medical supervision department can provide a meal plan supervised by a physician. The medical supervision department can also provide an exercise plan supervised by a physician. The medical supervision department can also provide a mental health plan supervised by a physician. This allows the medical supervision department to ensure that users can implement the plan with confidence. Some or all of the above processes in the medical supervision department may be performed using AI or not. For example, the medical supervision department can input a physician-supervised plan into an AI, which can then provide the user with the most suitable plan.

[0096] An incentive provision unit that awards points for continuing health management. The incentive provision unit can award points for continuing health management. For example, the incentive provision unit may award points when a user achieves a certain amount of exercise. For example, the incentive provision unit may also award points when a user continues to eat a healthy diet. For example, the incentive provision unit may also award points when a user implements a mental health plan. This allows the incentive provision unit to increase the user's motivation to continue health management. Some or all of the above processing in the incentive provision unit may be performed using AI or not. For example, the incentive provision unit may input the user's health management data into AI, and the AI ​​may calculate and award points.

[0097] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed health data. For example, if the user is in a hurry, the data collection unit can minimize the frequency of data collection to quickly obtain the necessary data. In this way, the data collection unit can adjust the frequency of data collection according to the user's emotions and reduce their burden. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and adjust the frequency of data collection.

[0098] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and collect heart rate data at the optimal time. For example, the data collection unit can analyze the user's past step count data and adjust the frequency of step count collection. For example, the data collection unit can analyze the user's past sleep patterns and optimize the method of collecting sleep data. This allows the data collection unit to select the optimal data collection method based on past data and improve accuracy. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past health data into AI, which can then select the optimal data collection method.

[0099] The data collection unit can dynamically change the types of data it collects based on the user's current activity level during data collection. For example, if the user is exercising, the data collection unit will prioritize collecting heart rate and step count data. If the user is resting, the data collection unit can also collect sleep data and stress level data. If the user is eating, the data collection unit can also collect photos of the meal and calorie intake data. This allows the data collection unit to efficiently collect data by changing the types of data it collects according to the user's activity level. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user activity data into the AI, and the types of data the AI ​​collects can be dynamically changed.

[0100] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting stress level data. For example, if the user is relaxed, the data collection unit may prioritize collecting heart rate and sleep data. For example, if the user is in a hurry, the data collection unit may prioritize collecting step count and exercise data. In this way, the data collection unit can determine the priority of data to collect according to the user's emotions and prioritize the collection of important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of the data.

[0101] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a park, the data collection unit can prioritize the collection of exercise data and heart rate. For example, if the user is at home, the data collection unit can prioritize the collection of sleep data and stress levels. For example, if the user is in a restaurant, the data collection unit can prioritize the collection of food photos and calorie intake data. This allows the data collection unit to collect highly relevant data based on the user's location information and improve accuracy. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's location information data into AI, which can then prioritize the collection of highly relevant data.

[0102] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. For example, if the user posts about food, the data collection unit can also collect photos of meals and calorie intake data. For example, if the user posts about stress, the data collection unit can also collect stress level data. This allows the data collection unit to collect relevant data based on the user's social media activity and improve accuracy. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into AI, and the AI ​​can collect relevant data.

[0103] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can enhance the stress level analysis algorithm. For example, if the user is relaxed, the analysis unit can also enhance the heart rate and sleep data analysis algorithm. For example, if the user is in a hurry, the analysis unit can also enhance the exercise data analysis algorithm. In this way, the analysis unit can adjust the analysis algorithm according to the user's emotions and improve the accuracy of the analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can estimate the emotions and adjust the analysis algorithm.

[0104] The analysis unit can improve the accuracy of its analysis by referring to past health data during the analysis process. For example, the analysis unit can refer to the user's past heart rate data to improve the accuracy of its current heart rate analysis. The analysis unit can also refer to the user's past step count data to improve the accuracy of its current step count analysis. The analysis unit can also refer to the user's past sleep patterns to improve the accuracy of its current sleep data analysis. In this way, the analysis unit can improve the accuracy of its analysis based on past data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past health data into an AI, which can then analyze the data to improve accuracy.

[0105] The analysis unit can apply different analysis methods based on the user's lifestyle during analysis. For example, if the user has an exercise habit, the analysis unit will apply an analysis method for exercise data. For example, if the user is mindful of their diet, the analysis unit may also apply an analysis method for diet data. For example, if the user prioritizes stress management, the analysis unit may also apply an analysis method for stress data. This allows the analysis unit to apply analysis methods according to the user's lifestyle and improve the accuracy of the analysis. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's lifestyle data into the AI, which can then select and apply an appropriate analysis method.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, which can estimate the emotions and adjust the display method.

[0107] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, the analysis unit can adjust the analysis of heart rate data if the user is at high altitude. For example, the analysis unit can also adjust the analysis of step count data if the user is in an urban area. For example, the analysis unit can adjust the analysis of sleep data if the user is at home. This allows the analysis unit to perform analysis based on the user's location information and improve accuracy. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's location information data into AI, and the AI ​​can analyze the data to improve accuracy.

[0108] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest research papers on the user's health status. The analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the user's lifestyle. The analysis unit can also improve the accuracy of its analysis by referring to literature on the user's mental health. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's relevant literature data into AI, and the AI ​​can analyze the literature to improve accuracy.

[0109] The feedback provider can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback provider can provide feedback to help them relax. For example, if the user is relaxed, the feedback provider can also provide feedback to help them maintain their health. For example, if the user is in a hurry, the feedback provider can provide feedback that can be acted upon quickly. In this way, the feedback provider can adjust the content of the feedback according to the user's emotions and provide more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback provider may be performed using AI or not using AI. For example, the feedback provider can input user emotion data into an AI, which can estimate the emotion and adjust the content of the feedback.

[0110] The feedback provider can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback provider can refer to feedback the user has received in the past and provide optimal feedback in similar situations. For example, the feedback provider can select effective feedback from the user's past feedback history. For example, the feedback provider can analyze the user's past feedback history and provide the most appropriate feedback. In this way, the feedback provider can provide optimal feedback based on past feedback history and enhance its effectiveness. Some or all of the above processes in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's past feedback history data into AI, and the AI ​​can select and provide optimal feedback.

[0111] The feedback provider can adjust the frequency of feedback based on the user's current health status when providing feedback. For example, if the user's health is good, the feedback provider can reduce the frequency of feedback. For example, if the user's health is deteriorating, the feedback provider can also increase the frequency of feedback. For example, if the user is trying to achieve a specific health goal, the feedback provider can adjust the frequency of feedback to match that goal. This allows the feedback provider to adjust the frequency of feedback according to the user's health status and provide feedback at the appropriate time. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's health status data into the AI, which can then adjust the frequency of feedback.

[0112] The feedback provider can estimate the user's emotions and prioritize feedback based on the estimated emotions. For example, if the user is stressed, the feedback provider will prioritize stress-reducing feedback. If the user is relaxed, the feedback provider may also prioritize health-maintaining feedback. If the user is in a hurry, the feedback provider may also prioritize feedback that can be acted upon quickly. This allows the feedback provider to prioritize feedback according to the user's emotions and provide important feedback preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the feedback provider can input user emotion data into an AI, which can estimate the emotions and determine the priority of feedback.

[0113] The feedback provider can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, if the user is in a park, the feedback provider can provide feedback related to exercise. For example, if the user is at home, the feedback provider can also provide feedback related to relaxation. For example, if the user is in a restaurant, the feedback provider can also provide feedback related to food. In this way, the feedback provider can provide optimal feedback based on the user's location information and enhance its effectiveness. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's location data into AI, and the AI ​​can select and provide the optimal feedback.

[0114] The feedback provider can analyze the user's social media activity and adjust the content of the feedback when providing it. For example, if the user posts about exercise on social media, the feedback provider can provide feedback about exercise. For example, if the user posts about food, the feedback provider can also provide feedback about food. For example, if the user posts about stress, the feedback provider can also provide feedback on stress reduction. In this way, the feedback provider can adjust the content of the feedback based on the user's social media activity and enhance its effectiveness. Some or all of the above processing in the feedback provider may be performed using AI or not. For example, the feedback provider can input the user's social media data into AI, and the AI ​​can adjust the content of the feedback.

[0115] The image generation unit can estimate the user's emotions and adjust the style of the generated images based on the estimated emotions. For example, if the user is relaxed, the image generation unit can generate images with calm colors and a soft design. If the user is excited, for example, the image generation unit can also generate images with vibrant colors and a dynamic design. If the user is stressed, for example, the image generation unit can also generate images with calm colors and a simple design. In this way, the image generation unit can adjust the style of images according to the user's emotions and enhance the visual effect. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image generation unit may be performed using AI or not. For example, the image generation unit can input user emotion data into an AI, which can estimate the emotion and adjust the style of the image.

[0116] The image generation unit can improve the accuracy of image generation by referring to the user's past health data during image generation. For example, the image generation unit can refer to the user's past weight data to accurately predict the body shape after implementing a diet plan. For example, the image generation unit can also refer to the user's past exercise data to accurately predict muscle development. For example, the image generation unit can refer to the user's past diet data to accurately predict the health status after improving dietary habits. This allows the image generation unit to improve the accuracy of image generation based on past health data. Some or all of the above-described processes in the image generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the image generation unit can input the user's past health data into a generation AI, which can then analyze the data to improve the accuracy of image generation.

[0117] The image generation unit can apply different generation methods based on the user's lifestyle habits when generating images. For example, if the user has an exercise habit, the image generation unit can generate an image that emphasizes the user's physique after exercise. For example, if the user is mindful of their diet, the image generation unit can also generate an image that emphasizes their health after improving their diet. For example, if the user prioritizes stress management, the image generation unit can also generate an image that emphasizes a relaxed state. In this way, the image generation unit can apply a generation method according to the user's lifestyle habits and improve generation accuracy. Some or all of the above-described processes in the image generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the image generation unit can input the user's lifestyle data into a generation AI, which can then select and apply an appropriate generation method.

[0118] The image generation unit can estimate the user's emotions and determine the priority of images to generate based on the estimated emotions. For example, if the user is relaxed, the image generation unit will prioritize generating images that emphasize relaxation. For example, if the user is excited, the image generation unit may also prioritize generating images that emphasize dynamism. For example, if the user is stressed, the image generation unit may also prioritize generating images that emphasize calmness. In this way, the image generation unit can determine the priority of images to generate according to the user's emotions and prioritize the generation of important images. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image generation unit may be performed using the generation AI or not. For example, the image generation unit can input user emotion data into the generation AI, which can estimate the emotions and determine the priority of images.

[0119] The image generation unit can generate the optimal image by considering the user's geographical location information during image generation. For example, if the user is at the beach, the image generation unit can generate an image with a beach landscape as the background. For example, if the user is in a mountainous area, the image generation unit can also generate an image with a mountain landscape as the background. For example, if the user is in an urban area, the image generation unit can also generate an image with an urban landscape as the background. In this way, the image generation unit can generate the optimal image based on the user's location information and enhance the visual effect. Some or all of the above processing in the image generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the image generation unit can input the user's location information data into a generation AI, and the generation AI can generate the optimal image.

[0120] The image generation unit can analyze the user's social media activity and adjust the content of the generated image during the generation process. For example, if the user posts about exercise on social media, the image generation unit can generate an image that emphasizes the user's physique after exercise. For example, if the user posts about food, the image generation unit can also generate an image that emphasizes the user's health after improving their diet. For example, if the user posts about stress, the image generation unit can also generate an image that emphasizes a relaxed state. In this way, the image generation unit can adjust the content of the generated image based on the user's social media activity and enhance its visual effect. Some or all of the above-described processes in the image generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the image generation unit can input the user's social media data into a generation AI, which can then adjust the content of the generated image.

[0121] The NLP analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the NLP analysis unit can enhance the stress level analysis algorithm. For example, if the user is relaxed, the NLP analysis unit can also enhance the emotional state analysis algorithm. For example, if the user is in a hurry, the NLP analysis unit can apply an algorithm that allows for rapid analysis. In this way, the NLP analysis unit can adjust the analysis algorithm according to the user's emotions and improve the accuracy of the analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input user emotion data into AI, which can estimate emotions and adjust the analysis algorithm.

[0122] The NLP analysis unit can improve the accuracy of its analysis by referring to the user's past text and voice data during NLP analysis. For example, the NLP analysis unit can refer to the user's past text data to improve the accuracy of its analysis of the user's current emotional state. The NLP analysis unit can also refer to the user's past voice data to improve the accuracy of its analysis of the user's current stress level. The NLP analysis unit can also refer to the user's past message history to analyze changes in emotional state. This allows the NLP analysis unit to improve the accuracy of its analysis based on past data. Some or all of the above processes in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's past text and voice data into an AI, which can then analyze the data to improve accuracy.

[0123] The NLP analysis unit can apply different analysis methods based on the user's lifestyle habits during NLP analysis. For example, if the user has an exercise habit, the NLP analysis unit can apply analysis methods related to exercise-related text and audio data. For example, if the user is mindful of their diet, the NLP analysis unit can also apply analysis methods related to diet-related text and audio data. For example, if the user prioritizes stress management, the NLP analysis unit can also apply analysis methods related to stress-related text and audio data. This allows the NLP analysis unit to apply analysis methods according to the user's lifestyle habits and improve analysis accuracy. Some or all of the above-described processes in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's lifestyle data into AI, which can then select and apply an appropriate analysis method.

[0124] The NLP analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the NLP analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the NLP analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the NLP analysis unit can also provide a display method that gets straight to the point. In this way, the NLP analysis unit can adjust the display method of the analysis results according to the user's emotions and improve visibility. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input user emotion data into an AI, which can estimate the emotions and adjust the display method.

[0125] The NLP analysis unit can perform NLP analysis while taking into account the user's geographical location information. For example, if the user is at high altitude, the NLP analysis unit can adjust the analysis of heart rate data. For example, if the user is in an urban area, the NLP analysis unit can also adjust the analysis of step count data. For example, if the user is at home, the NLP analysis unit can also adjust the analysis of sleep data. This allows the NLP analysis unit to perform analysis based on the user's location information and improve accuracy. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's location information data into AI, and the AI ​​can analyze the data to improve accuracy.

[0126] The NLP analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during NLP analysis. For example, the NLP analysis unit can improve the accuracy of its analysis by referring to the latest research papers on the user's health status. The NLP analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the user's lifestyle habits. The NLP analysis unit can also improve the accuracy of its analysis by referring to literature on the user's mental health. In this way, the NLP analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the NLP analysis unit may be performed using AI or not. For example, the NLP analysis unit can input the user's relevant literature data into AI, and the AI ​​can analyze the literature to improve accuracy.

[0127] The meal analysis unit can estimate the user's emotions and adjust the meal analysis algorithm based on the estimated emotions. For example, if the user is stressed, the meal analysis unit can enhance the meal analysis algorithm to help reduce stress. For example, if the user is relaxed, the meal analysis unit can also enhance the meal analysis algorithm to help maintain health. For example, if the user is in a hurry, the meal analysis unit can apply a meal analysis algorithm that can be analyzed quickly. In this way, the meal analysis unit can adjust the meal analysis algorithm according to the user's emotions and improve the accuracy of the analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input user emotion data into AI, and the AI ​​can estimate the emotions and adjust the meal analysis algorithm.

[0128] The meal analysis unit can improve the accuracy of its analysis by referring to the user's past meal data during meal analysis. For example, the meal analysis unit can refer to the user's past meal data to improve the accuracy of its analysis of the current meal. For example, the meal analysis unit can also refer to the user's past calorie data to improve the accuracy of its analysis of the current calorie intake. For example, the meal analysis unit can refer to the user's past nutrition data to improve the accuracy of its analysis of the current nutritional balance. In this way, the meal analysis unit can improve the accuracy of its analysis based on past data. Some or all of the above processes in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input the user's past meal data into AI, and the AI ​​can analyze the data to improve accuracy.

[0129] The diet analysis unit can apply different analysis methods based on the user's lifestyle during diet analysis. For example, if the user has an exercise habit, the diet analysis unit can apply an analysis method related to exercise-related dietary data. For example, if the user is mindful of their diet, the diet analysis unit can also apply an analysis method related to diet-related data. For example, if the user prioritizes stress management, the diet analysis unit can also apply an analysis method related to stress-related dietary data. This allows the diet analysis unit to apply analysis methods according to the user's lifestyle and improve analysis accuracy. Some or all of the above processing in the diet analysis unit may be performed using AI or not. For example, the diet analysis unit can input the user's lifestyle data into AI, which can then select and apply an appropriate analysis method.

[0130] The meal analysis unit can estimate the user's emotions and determine the priority of meal analysis based on the estimated emotions. For example, if the user is stressed, the meal analysis unit will prioritize meals that help reduce stress. For example, if the user is relaxed, the meal analysis unit may also prioritize meals that help maintain health. For example, if the user is in a hurry, the meal analysis unit may also prioritize meals that can be analyzed quickly. In this way, the meal analysis unit can determine the priority of meal analysis according to the user's emotions and perform important analyses first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of meal analysis.

[0131] The meal analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user is at high altitude, the meal analysis unit can adjust the analysis of the nutritional balance of the meal. For example, if the user is in an urban area, the meal analysis unit can also adjust the analysis of the calorie intake of the meal. For example, if the user is at home, the meal analysis unit can also adjust the analysis of the nutritional balance of the meal. This allows the meal analysis unit to perform analysis based on the user's location information and improve accuracy. Some or all of the above processing in the meal analysis unit may be performed using AI or not. For example, the meal analysis unit can input the user's location information data into AI, and the AI ​​can analyze the data to improve accuracy.

[0132] The diet analysis unit can improve the accuracy of its analysis by referring to relevant literature for the user during the analysis. For example, the diet analysis unit can improve the accuracy of its analysis by referring to the latest research papers on the user's diet. The diet analysis unit can also improve the accuracy of its analysis by referring to relevant literature on the user's nutritional balance. The diet analysis unit can also improve the accuracy of its analysis by referring to literature on the user's calorie intake. In this way, the diet analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the diet analysis unit may be performed using AI or not. For example, the diet analysis unit can input the user's relevant literature data into the AI, and the AI ​​can analyze the literature to improve accuracy.

[0133] The medical supervision department can estimate the user's emotions and adjust the content of medical supervision based on the estimated emotions. For example, if the user is stressed, the medical supervision department can provide medical supervision that helps reduce stress. For example, if the user is relaxed, the medical supervision department can also provide medical supervision that helps maintain health. For example, if the user is in a hurry, the medical supervision department can also provide medical supervision that can be performed quickly. In this way, the medical supervision department can adjust the content of medical supervision according to the user's emotions and provide more appropriate medical supervision. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the medical supervision department may be performed using AI or not using AI. For example, the medical supervision department can input user emotion data into AI, and the AI ​​can estimate the emotions and adjust the content of medical supervision.

[0134] The medical supervision department can improve the accuracy of medical supervision by referring to the user's past medical data during medical supervision. For example, the medical supervision department can refer to the user's past medical records to improve the accuracy of current medical supervision. The medical supervision department can also refer to the user's past health checkup data to improve the accuracy of current medical supervision. The medical supervision department can also refer to the user's past prescription history to improve the accuracy of current medical supervision. In this way, the medical supervision department can improve the accuracy of supervision based on past medical data. Some or all of the above processes in the medical supervision department may be performed using AI or not. For example, the medical supervision department can input the user's past medical data into AI, and the AI ​​can analyze the data to improve accuracy.

[0135] The Medical Supervision Department can apply different supervision methods based on the user's lifestyle during medical supervision. For example, if the user has an exercise habit, the Medical Supervision Department can apply exercise-related medical supervision methods. For example, if the user is mindful of their diet, the Medical Supervision Department can also apply diet-related medical supervision methods. For example, if the user places importance on stress management, the Medical Supervision Department can also apply stress-related medical supervision methods. This allows the Medical Supervision Department to apply supervision methods according to the user's lifestyle and improve the accuracy of supervision. Some or all of the above processes in the Medical Supervision Department may be performed using AI or not. For example, the Medical Supervision Department can input the user's lifestyle data into AI, which can then select and apply an appropriate supervision method.

[0136] The medical supervision department can estimate the user's emotions and determine the priority of medical supervision based on the estimated emotions. For example, if the user is stressed, the medical supervision department will prioritize medical supervision that helps reduce stress. For example, if the user is relaxed, the medical supervision department may also prioritize medical supervision that helps maintain health. For example, if the user is in a hurry, the medical supervision department may also prioritize medical supervision that can be performed quickly. This allows the medical supervision department to determine the priority of medical supervision according to the user's emotions and to prioritize important supervision. 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. Some or all of the above processing in the medical supervision department may be performed using AI or not. For example, the medical supervision department can input user emotion data into AI, which can estimate emotions and determine the priority of medical supervision.

[0137] The Medical Supervision Department can provide optimal supervision during medical supervision by taking into account the user's geographical location. For example, if the user is at high altitude, the Medical Supervision Department can provide medical supervision appropriate for high altitude. For example, if the user is in an urban area, the Medical Supervision Department can also provide medical supervision appropriate for urban areas. For example, if the user is at home, the Medical Supervision Department can also provide medical supervision appropriate for home use. This allows the Medical Supervision Department to provide optimal supervision based on the user's location information and enhance its effectiveness. Some or all of the above processing in the Medical Supervision Department may be performed using AI or not. For example, the Medical Supervision Department can input the user's location data into AI, and the AI ​​can analyze the data to provide optimal supervision.

[0138] The medical supervision department can improve the accuracy of its supervision by referring to relevant literature on the user during medical supervision. For example, the medical supervision department can improve the accuracy of its supervision by referring to the latest research papers on the user's health status. The medical supervision department can also improve the accuracy of its supervision by referring to relevant literature on the user's lifestyle. The medical supervision department can also improve the accuracy of its supervision by referring to literature on the user's mental health. In this way, the medical supervision department can improve the accuracy of its supervision by referring to relevant literature. Some or all of the above processes in the medical supervision department may be performed using AI or not. For example, the medical supervision department can input the user's relevant literature data into AI, and the AI ​​can analyze the literature to improve accuracy.

[0139] The incentive provider can estimate the user's emotions and adjust the content of the incentives based on the estimated emotions. For example, if the user is stressed, the incentive provider can provide incentives that help them relax. For example, if the user is relaxed, the incentive provider can also provide incentives that help maintain their health. For example, if the user is in a hurry, the incentive provider can also provide incentives that can be used quickly. In this way, the incentive provider can adjust the content of the incentives according to the user's emotions and provide more appropriate incentives. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the incentive provider may be performed using AI or not using AI. For example, the incentive provider can input user emotion data into AI, and the AI ​​can estimate the emotions and adjust the content of the incentives.

[0140] The incentive provision unit can provide the most suitable incentive by referring to the user's past incentive history when providing incentives. For example, the incentive provision unit can refer to incentives the user has received in the past and provide the most suitable incentive in a similar situation. For example, the incentive provision unit can select an effective incentive from the user's past incentive history. For example, the incentive provision unit can analyze the user's past incentive history and provide the most appropriate incentive. In this way, the incentive provision unit can provide the most suitable incentive based on past incentive history and enhance its effectiveness. Some or all of the above processes in the incentive provision unit may be performed using AI or not. For example, the incentive provision unit can input the user's past incentive history data into AI, and the AI ​​can select and provide the most suitable incentive.

[0141] The incentive provider can adjust the frequency of incentives based on the user's current health status when providing incentives. For example, if the user's health is good, the incentive provider can reduce the frequency of incentives. For example, if the user's health is deteriorating, the incentive provider can also increase the frequency of incentives. For example, if the user is trying to achieve a specific health goal, the incentive provider can adjust the frequency of incentives to match that goal. This allows the incentive provider to adjust the frequency of incentives according to the user's health status and provide incentives at the appropriate time. Some or all of the above processing in the incentive provider may be performed using AI or not. For example, the incentive provider can input user health data into AI, and the AI ​​can adjust the frequency of incentives.

[0142] The incentive provider can estimate the user's emotions and determine the priority of incentives based on the estimated emotions. For example, if the user is stressed, the incentive provider may prioritize incentives that help reduce stress. For example, if the user is relaxed, the incentive provider may also prioritize incentives that help maintain health. For example, if the user is in a hurry, the incentive provider may also prioritize incentives that can be used quickly. This allows the incentive provider to determine the priority of incentives according to the user's emotions and provide important incentives preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the incentive provider may be performed using AI or not. For example, the incentive provider can input user emotion data into an AI, which can estimate the emotions and determine the priority of incentives.

[0143] The incentive provider can provide the most suitable incentives by considering the user's geographical location when providing incentives. For example, if the user is in a park, the incentive provider can provide incentives related to exercise. For example, if the user is at home, the incentive provider can also provide incentives for relaxation. For example, if the user is in a restaurant, the incentive provider can also provide incentives related to dining. This allows the incentive provider to provide the most suitable incentives based on the user's location information, thereby enhancing their effectiveness. Some or all of the above processing in the incentive provider may be performed using AI or not. For example, the incentive provider can input the user's location data into AI, which can then provide the most suitable incentives.

[0144] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0145] A healthcare assistant system can estimate a user's emotions and adjust the content of the feedback based on those emotions. For example, if a user is stressed, it can provide feedback to help them relax. If the user is relaxed, it can provide feedback to help them maintain their health. Furthermore, if the user is in a hurry, it can provide feedback that can be acted upon quickly. In this way, the feedback provider can adjust the content of the feedback according to the user's emotions and provide more appropriate feedback.

[0146] The healthcare assistant system can analyze a user's past health data and select the optimal data collection method. For example, it can analyze a user's past heart rate data and collect heart rate data at the optimal time. It can also analyze a user's past step count data and adjust the frequency of step count collection. Furthermore, it can analyze a user's past sleep patterns and optimize the sleep data collection method. As a result, the data collection unit can select the optimal data collection method based on past data and improve accuracy.

[0147] The healthcare assistant system can estimate the user's emotions and adjust the frequency of data collection based on those emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed health data. Furthermore, if the user is in a hurry, the frequency of data collection can be minimized to quickly obtain the necessary data. In this way, the data collection unit can adjust the frequency of data collection according to the user's emotions, thereby reducing their burden.

[0148] The healthcare assistant system can prioritize the collection of relevant data by considering the user's geographical location. For example, if the user is in a park, it can prioritize the collection of exercise data and heart rate. If the user is at home, it can prioritize the collection of sleep data and stress levels. Furthermore, if the user is in a restaurant, it can prioritize the collection of food photos and calorie intake data. This allows the data collection unit to collect more relevant data based on the user's location, improving accuracy.

[0149] The healthcare assistant system can estimate the user's emotions and adjust its analysis algorithms based on those emotions. For example, if the user is stressed, the stress level analysis algorithm can be enhanced. Similarly, if the user is relaxed, the heart rate and sleep data analysis algorithms can be enhanced. Furthermore, if the user is in a hurry, the exercise data analysis algorithm can be enhanced. This allows the analysis unit to adjust its algorithms according to the user's emotions, thereby improving analysis accuracy.

[0150] The healthcare assistant system can apply different analysis methods based on the user's lifestyle during analysis. For example, if the user has an exercise habit, an exercise data analysis method can be applied. Similarly, if the user is mindful of their diet, a diet data analysis method can be applied. Furthermore, if the user prioritizes stress management, a stress data analysis method can be applied. This allows the analysis unit to apply the appropriate analysis method according to the user's lifestyle, thereby improving the accuracy of the analysis.

[0151] The healthcare assistant system can estimate the user's emotions and adjust the display method of the analysis results based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions, thereby improving visibility.

[0152] A healthcare assistant system can provide optimal feedback by referring to a user's past feedback history. For example, it can refer to feedback a user has received in the past and provide the most appropriate feedback in similar situations. It can also select effective feedback from a user's past feedback history. Furthermore, it can analyze a user's past feedback history and provide the most appropriate feedback. As a result, the feedback delivery unit can provide optimal feedback based on past feedback history, thereby enhancing its effectiveness.

[0153] A healthcare assistant system can estimate a user's emotions and prioritize feedback based on those emotions. For example, if a user is stressed, stress-reducing feedback can be prioritized. If a user is relaxed, health-maintaining feedback can be prioritized. Furthermore, if a user is in a hurry, feedback that can be acted upon quickly can be prioritized. This allows the feedback provider to prioritize feedback according to the user's emotions and deliver important feedback preferentially.

[0154] The healthcare assistant system can provide the most suitable incentives by referring to the user's past incentive history. For example, it can refer to incentives the user has received in the past and provide the most suitable incentive in a similar situation. It can also select effective incentives from the user's past incentive history. Furthermore, it can analyze the user's past incentive history and provide the most appropriate incentive. As a result, the incentive provision unit can provide the most suitable incentives based on past incentive history, thereby increasing effectiveness.

[0155] The following briefly describes the processing flow for example form 2.

[0156] Step 1: The data collection unit collects real-time data from wearable devices. The data collection unit can collect data such as heart rate, steps, and sleep patterns. For example, the data collection unit can acquire heart rate data from a smartwatch. It can also collect step count data from a pedometer. Furthermore, it can collect sleep pattern data from a sleep tracker. Step 2: The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. For example, the analysis unit can analyze heart rate data to evaluate the user's cardiac health. It can also analyze step count data to evaluate the user's exercise level. Furthermore, the analysis unit can analyze sleep pattern data to evaluate the user's sleep quality. Step 3: The feedback unit provides immediate feedback based on the health status assessed by the analysis unit. For example, the feedback unit can provide notifications encouraging exercise if the user is not getting enough exercise. It can also provide notifications encouraging relaxation if the user has a high heart rate. Furthermore, it can provide advice on improving sleep if the user has poor sleep quality. Step 4: The image generation unit visualizes the post-plan implementation state based on the feedback provided by the feedback provision unit. For example, the image generation unit can predict the body shape after implementing a diet plan and display it as an image. It can also predict muscle development after implementing a strength training plan and display it as an image. Furthermore, it can predict a relaxed state after implementing a stress reduction plan and display it as an image. Step 5: The NLP analysis unit analyzes the user's mental state based on the images generated by the image generation unit. For example, the NLP analysis unit can analyze text and voice data entered by the user to evaluate stress levels and emotional states. The NLP analysis unit can also suggest breathing exercises or meditation techniques to help the user relax based on their mental state. Furthermore, the NLP analysis unit can provide advice for stress reduction based on the user's mental state.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, image generation unit, NLP analysis unit, meal analysis unit, medical supervision unit, and incentive provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects data such as heart rate, steps, and sleep patterns. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The feedback provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides immediate feedback based on the analysis results. The image generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and visualizes the user's state after the plan is implemented. The NLP analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's mental state. The meal analysis unit analyzes a photograph of a meal using the camera 42 of the smart device 14 and calculates calories. The medical supervision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a plan supervised by a physician. The incentive provision unit is implemented, for example, by the control unit 46A of the smart device 14, and awards points for continued health management. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0161] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, image generation unit, NLP analysis unit, meal analysis unit, medical supervision unit, and incentive provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects data such as heart rate, steps, and sleep patterns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The feedback provision unit is implemented by the control unit 46A of the smart glasses 214 and provides immediate feedback based on the analysis results. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the user's appearance after the plan is implemented. The NLP analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's mental state. The meal analysis unit analyzes a photograph of a meal using the camera 42 of the smart glasses 214 and calculates calories. The medical supervision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a plan supervised by a physician. The incentive provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and awards points for continued health management. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0177] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.).

[0189] 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.

[0190] 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.

[0191] 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.

[0192] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, image generation unit, NLP analysis unit, meal analysis unit, medical supervision unit, and incentive provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects data such as heart rate, steps, and sleep patterns. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The feedback provision unit is implemented by the control unit 46A of the headset terminal 314 and provides immediate feedback based on the analysis results. The image generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and visualizes the user's appearance after the plan is implemented. The NLP analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's mental state. The meal analysis unit analyzes a photograph of a meal using the camera 42 of the headset terminal 314 and calculates calories. The medical supervision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a plan supervised by a physician. The incentive provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and awards points for continued health management. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0193] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] Each of the multiple elements described above, including the data collection unit, analysis unit, feedback provision unit, image generation unit, NLP analysis unit, meal analysis unit, medical supervision unit, and incentive provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects data such as heart rate, steps, and sleep patterns. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The feedback provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides immediate feedback based on the analysis results. The image generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and visualizes the user's appearance after the plan has been implemented. The NLP analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's mental state. The meal analysis unit analyzes a photograph of a meal using the camera 42 of the robot 414 and calculates the calories. The medical supervision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides a plan supervised by a physician. The incentive provision unit is implemented, for example, by the control unit 46A of the robot 414, and awards points for continued health management. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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."

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] (Note 1) A data collection unit that collects real-time data from wearable devices, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the user's health status, A feedback provision unit provides immediate feedback based on the health status evaluated by the analysis unit, An image generation unit visualizes the state after the plan is implemented based on the feedback provided by the aforementioned feedback provision unit, The system includes an NLP analysis unit that analyzes the user's mental state based on the image generated by the image generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as heart rate, steps taken, and sleep patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback provision unit, Provides notifications to encourage exercise if you are not getting enough exercise. The system described in Appendix 1, characterized by the features described herein. (Note 4) The image generation unit, Predicts your body shape after the plan is implemented and displays it as an image. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned NLP analysis unit, The system analyzes text and voice data entered by the user to assess their stress level and emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned NLP analysis unit, We offer suggestions for breathing techniques and meditation to help you relax. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a meal analysis unit that analyzes photos of meals and calculates calories. The system described in Appendix 1, characterized by the features described herein. (Note 8) We have a medical supervision department that provides plans supervised by doctors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The company has an incentive department that awards points for continuing to maintain good health. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and adjusts the frequency of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the types of data collected are dynamically changed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, past health data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, different analysis methods are applied based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback provision unit, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback provision unit, When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback provision unit, When providing feedback, adjust the frequency of feedback based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback provision unit, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback provision unit, When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback provision unit, When providing feedback, we analyze the user's social media activity and adjust the content of the feedback accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 28) The image generation unit, It estimates the user's emotions and adjusts the style of the generated images based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The image generation unit, When generating images, the system improves the accuracy of the generation by referencing the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The image generation unit, When generating images, different generation methods are applied based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 31) The image generation unit, It estimates the user's emotions and determines the priority of images to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The image generation unit, When generating images, the system takes the user's geographical location information into consideration to generate the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 33) The image generation unit, When generating images, the system analyzes the user's social media activity and adjusts the generated content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned NLP analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned NLP analysis unit, During NLP analysis, referencing the user's past text and voice data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned NLP analysis unit, During NLP analysis, different analysis methods are applied based on the user's lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned NLP analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned NLP analysis unit, When performing NLP analysis, the analysis takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned NLP analysis unit, When performing NLP analysis, referencing relevant user literature improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned dietary analysis unit, The system estimates the user's emotions and adjusts the meal analysis algorithm based on those estimated emotions. The system according to appended note 1, characterized in that... (Appended note 41) The diet analysis unit Improves the accuracy of analysis by referring to the user's past diet data during diet analysis The system according to appended note 1, characterized in that... (Appended note 42) The diet analysis unit Applies different analysis methods based on the user's lifestyle during diet analysis The system according to appended note 1, characterized in that... (Appended note 43) The diet analysis unit Estimates the user's emotion and determines the priority of diet analysis based on the estimated user emotion The system according to appended note 1, characterized in that... (Appended note 44) The diet analysis unit Performs analysis considering the user's geographical location information during diet analysis The system according to appended note 1, characterized in that... (Appended note 45) The diet analysis unit Improves the accuracy of analysis by referring to the user's related literature during diet analysis The system according to appended note 1, characterized in that... (Appended note 46) The medical supervision unit Estimates the user's emotion and adjusts the content of medical supervision based on the estimated user emotion The system according to appended note 1, characterized in that... (Appended note 47) The medical supervision unit Improves the accuracy of supervision by referring to the user's past medical data during medical supervision The system according to appended note 1, characterized in that... (Appended note 48) The medical supervision unit Applies different supervision methods based on the user's lifestyle during medical supervision The system according to appended note 1, characterized in that... (Note 49) The aforementioned medical supervision department, It estimates user emotions and determines medical supervision priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned medical supervision department, During medical supervision, the system provides optimal supervision by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned medical supervision department, During medical supervision, refer to relevant user literature to improve the accuracy of supervision. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned incentive provision unit, The system estimates the user's emotions and adjusts the incentive content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned incentive provision unit, When providing incentives, we refer to the user's past incentive history to provide the most suitable incentive. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned incentive provision unit, When providing incentives, adjust the frequency of incentives based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned incentive provision unit, It estimates user emotions and determines incentive priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned incentive provision unit, When providing incentives, we will consider the user's geographical location to provide the most appropriate incentive. The system according to appended note 1, characterized by the following.

Explanation of symbols

[0229] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects real-time data from wearable devices, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the user's health status, A feedback provision unit provides immediate feedback based on the health status evaluated by the analysis unit, An image generation unit visualizes the state after the plan is implemented based on the feedback provided by the aforementioned feedback provision unit, The system includes an NLP analysis unit that analyzes the user's mental state based on the image generated by the image generation unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as heart rate, steps taken, and sleep patterns. The system according to feature 1.

3. The aforementioned feedback provision unit, Provides notifications to encourage exercise if you are not getting enough exercise. The system according to feature 1.

4. The image generation unit, Predicts your body shape after the plan is implemented and displays it as an image. The system according to feature 1.

5. The aforementioned NLP analysis unit, The system analyzes text and voice data entered by the user to assess their stress level and emotional state. The system according to feature 1.

6. The aforementioned NLP analysis unit, We offer suggestions for breathing techniques and meditation to help you relax. The system according to feature 1.

7. It includes a meal analysis unit that analyzes photos of meals and calculates calories. The system according to feature 1.

8. We have a medical supervision department that provides plans supervised by doctors. The system according to feature 1.

Images