system

Abstract

The system according to this embodiment aims to support users in making healthy lifestyle choices by analyzing their lifestyle habits and health data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a feedback unit. The collection unit collects the user's lifestyle and health data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes health suggestions based on the analysis results obtained by the analysis unit. The feedback unit provides visual feedback based on the suggestions made by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully done to analyze the user's living habits and health data in real time and provide visual feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's living habits and health data and support healthy life choices.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a feedback unit. The data collection unit collects the user's lifestyle and health data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes health suggestions based on the analysis results obtained by the analysis unit. The feedback unit provides visual feedback based on the suggestions made by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's lifestyle habits and health data to support healthy lifestyle choices. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network �4 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart glasses according to an embodiment of the present invention are a system that analyzes a user's lifestyle and health data and provides real-time visual feedback. These smart glasses collect the user's lifestyle and health data, and AI analyzes it to understand the user's health status. Based on the analysis results, the AI ​​provides personalized health suggestions and visual feedback. For example, it can detect fluctuations in blood glucose levels and recommend restaurants offering low-carbohydrate meals. It also automatically tracks lifestyle habits and suggests improvements, streamlining the user's health management. This is expected to lead to improved health, reduced medical costs, and prevention of lifestyle-related diseases. Furthermore, the integration of wearable devices and AI creates a sustainable health support system utilizing biometric data. The target audience is health-conscious individuals, those who want to prevent lifestyle-related diseases, and those who want to simplify their daily health management. Generative AI is used to analyze health data and generate personalized feedback. The market size is the wearable device and health management market, with increasing healthcare needs and technological advancements creating market opportunities. With these innovative smart glasses, we aim to revolutionize individual health management and realize a healthier society. This allows smart glasses to monitor the user's health in real time and provide appropriate feedback.

[0029] The smart glasses according to this embodiment include a data collection unit, an analysis unit, a suggestion unit, and a feedback unit. The data collection unit collects the user's lifestyle and health data. For example, the data collection unit can collect data such as the user's diet, exercise, and sleep. The data collection unit can also collect data using a wearable device or a smartphone application. For example, the data collection unit can collect health data such as the user's heart rate, blood pressure, and weight. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit can analyze the data using AI to understand the user's health status. The suggestion unit makes health suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can make suggestions for dietary improvements or recommend exercise. The suggestion unit can generate personalized health suggestions using AI. The feedback unit provides visual feedback based on the content suggested by the suggestion unit. For example, the feedback unit can provide feedback in the form of graphs, charts, animations, etc. The feedback unit can provide appropriate feedback to the user using AI. As a result, the smart glasses according to this embodiment can support healthy lifestyle choices by collecting, analyzing, suggesting, and providing feedback on the user's lifestyle and health data.

[0030] The data collection unit collects users' lifestyle and health data. Specifically, it can collect data on users' diet, exercise, sleep, and other aspects of their lives. The data collection unit can also collect data using wearable devices and smartphone apps. For example, a wearable device can monitor the user's health data, such as heart rate, blood pressure, and weight, in real time and transmit it to a smartphone app. This allows for continuous monitoring of the user's health status. Furthermore, the data collection unit has a function to automatically analyze calories and nutrients by taking photos of meals to record the user's diet. For exercise data, it accurately records the user's exercise volume and distance traveled using a pedometer and GPS function. For sleep data, it measures the user's sleep duration and sleep quality using sensors on wearable devices. This data is stored on a cloud server and later used by the analysis unit. To protect user privacy, the data collection unit encrypts and anonymizes data and manages it securely. In addition, the data collection unit collects data only with the user's consent, and users can stop data collection at any time if they wish. This allows the data collection unit to efficiently and securely collect users' lifestyle and health data, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it can analyze data using statistical analysis and machine learning algorithms. For example, statistical analysis calculates the mean and standard deviation of users' health data and detects outliers. By using machine learning algorithms, it can predict changes in users' health status and detect abnormal patterns early. AI-based data analysis analyzes users' health data from multiple perspectives and assesses health risks. For example, it analyzes heart rate and blood pressure data to assess stress levels and cardiovascular risk. It also analyzes dietary data to detect imbalances in nutrition. By analyzing exercise data, it understands users' exercise habits and warns of insufficient or excessive exercise. By analyzing sleep data, it evaluates the quality of users' sleep and suggests areas for improvement. The analysis department comprehensively analyzes this data to gain a holistic understanding of users' health status. Furthermore, by comparing past data and data from other users, the analysis department can grasp trends in users' health status and support long-term health management. In this way, the analysis department can accurately understand users' health status and provide the foundational data for making appropriate health suggestions.

[0032] The Proposal Department provides health recommendations based on the analysis results obtained by the Analysis Department. Specifically, it can offer suggestions for dietary improvements and exercise recommendations. For example, in dietary improvement suggestions, it identifies areas for improvement in nutritional balance and proposes specific meal menus based on the user's dietary data. In exercise recommendations, it suggests appropriate exercise volume and type based on the user's exercise data. The Proposal Department can generate personalized health recommendations using AI. The AI ​​learns the user's health data and lifestyle habits and provides suggestions tailored to the user's needs. For example, if the user wants to lose weight, it will suggest calorie restrictions and exercise plans. If the user is feeling stressed, it will suggest relaxation methods and stress management techniques. The Proposal Department can collect user feedback and continuously improve its recommendations. For example, it can record the results of actions taken by the user following the recommendations and evaluate their effectiveness. The Proposal Department can flexibly adjust its recommendations according to changes in the user's health condition and lifestyle habits. This allows the Proposal Department to provide users with optimal health recommendations and support them in making healthy lifestyle choices.

[0033] The Feedback Department provides visual feedback based on the suggestions made by the Proposal Department. Specifically, feedback can be provided in the form of graphs, charts, and animations. For example, it can graph the user's health data to visually show changes in their health status. It can use charts to display trends in dietary balance and exercise levels. It can use animations to clearly explain how to implement health suggestions. The Feedback Department can use AI to provide appropriate feedback to the user. The AI ​​learns the user's reactions and behaviors and optimizes the content and format of the feedback. For example, if the user prefers visual feedback, it will make extensive use of graphs and charts. If the user requests specific action instructions, it will provide animations and step-by-step guides. The Feedback Department can visually show achievement goals and progress to increase the user's motivation. For example, it can display progress toward goal achievement with a bar graph to give a sense of accomplishment. The Feedback Department can collect user feedback and continuously improve the accuracy and effectiveness of the feedback content. This allows the Feedback Department to provide effective visual feedback to users and support them in making healthy lifestyle choices.

[0034] The smart glasses are equipped with a detection unit that detects fluctuations in blood glucose levels. The detection unit allows for settings such as the frequency of blood glucose measurement and the threshold for fluctuations. The detection unit can use AI to detect blood glucose fluctuations in real time. For example, the detection unit can notify the user when it detects a change in blood glucose levels. This allows the system to understand the user's health status by detecting blood glucose fluctuations and provide appropriate suggestions.

[0035] The smart glasses feature a guidance unit that directs users to restaurants that recommend low-carbohydrate meals. The guidance unit can select restaurants based, for example, on carbohydrate content and recommended foods. It can also use AI to recommend restaurants suitable for the user. For example, it can suggest restaurants based on the user's current location and dietary preferences. This allows the smart glasses to support users' healthy eating habits by guiding them to restaurants that recommend low-carbohydrate meals.

[0036] Smart glasses are equipped with a tracking unit that automatically tracks lifestyle habits. The tracking unit allows users to configure, for example, the sensors to be used and the types of data to be tracked. The tracking unit can automatically track the user's lifestyle habits using AI. For example, the tracking unit can track the user's exercise level and sleep duration in real time. This allows for more efficient health management by automatically tracking lifestyle habits.

[0037] Smart glasses are equipped with an improvement unit that offers suggestions for improving lifestyle habits. This unit can, for example, recommend improvements to diet and exercise. The improvement unit can use AI to provide personalized improvement suggestions tailored to the user. For example, it can provide personalized suggestions based on the user's health condition and lifestyle habits. This allows for improvements in the user's health condition through lifestyle improvements.

[0038] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the most effective time of day for data collection based on the user's past health data. The data collection unit can also select a specific data collection method (e.g., wearable device, smartphone app) based on the user's past health data. The data collection unit can also optimize the collection frequency based on the user's past health data. This allows for efficient data collection by selecting the optimal collection method through analysis of the user's past health data.

[0039] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the unit will prioritize collecting data related to exercise. If the user is resting, the unit can also collect data related to relaxation. If the user is outdoors, the unit can also collect health data while considering environmental data (e.g., temperature, humidity). This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can collect altitude-related health data. If the user is in an urban area, the data collection unit can also collect data related to environmental pollution. If the user is indoors, the data collection unit can also collect data related to the indoor environment. In this way, by considering the user's geographical location, highly relevant health data can be collected.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the unit can collect data related to their stress level. If a user is relaxing on social media, the unit can also collect data related to their relaxation state. If a user is posting about exercise on social media, the unit can also collect data related to exercise. In this way, relevant health data can be collected by analyzing a user's social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit will perform a detailed analysis on important health data, while performing a simplified analysis on general health data. The analysis unit can also adjust the level of detail of the analysis according to the user's health status. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the health data.

[0043] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, it can apply a blood glucose variability analysis algorithm to blood glucose data. It can also apply a heart rate variability analysis algorithm to heart rate data. It can also apply a sleep quality evaluation algorithm to sleep data. By applying different analysis algorithms depending on the category of health data, more accurate analysis becomes possible.

[0044] The analysis department can prioritize analyses based on when health data was collected. For example, it might prioritize analyzing recently collected health data. It can also prioritize analyzing data collected during a specific period. The analysis department can also adjust the analysis priority according to the user's health status. This allows for efficient data analysis by prioritizing analyses based on when health data was collected.

[0045] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also adjust the order of analysis according to the user's health status. The analysis unit can also determine the order of analysis based on specific health data categories. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of health data.

[0046] The proposal department can adjust the level of detail in its proposals based on the importance of each health suggestion. For example, it can provide detailed suggestions for important health suggestions, while providing concise suggestions for general health suggestions. The proposal department can also adjust the level of detail according to the user's health status. This allows for more efficient proposals by adjusting the level of detail based on the importance of each health suggestion.

[0047] The suggestion function can apply different suggestion algorithms depending on the category of the health suggestion. For example, for a meal suggestion, it can apply a suggestion algorithm that considers the nutritional balance of the meal. For an exercise suggestion, it can also apply a suggestion algorithm that considers the effects of exercise. For a sleep suggestion, it can also apply a suggestion algorithm that improves sleep quality. By applying different suggestion algorithms depending on the category of the health suggestion, more appropriate suggestions can be made.

[0048] The proposal department can prioritize proposals based on when health data was collected. For example, the proposal department can make proposals based on recently collected health data. The proposal department can also make proposals based on data collected during a specific period. The proposal department can also adjust the priority of proposals according to the user's health status. This allows for more efficient proposals by prioritizing proposals based on when health data was collected.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of health data during the suggestion process. For example, the suggestion unit makes suggestions based on highly relevant data. The suggestion unit can also adjust the order of suggestions according to the user's health status. The suggestion unit can also determine the order of suggestions based on specific health data categories. This allows for more efficient suggestions by adjusting the order of suggestions based on the relevance of health data.

[0050] The feedback unit can provide optimal feedback by referencing the user's past health data during the feedback process. For example, the feedback unit can provide the most effective feedback based on the user's past health data. The feedback unit can also provide feedback on specific health conditions based on the user's past health data. Furthermore, the feedback unit can customize the content of the feedback based on the user's past health data. This allows the system to provide optimal feedback by referencing the user's past health data.

[0051] The feedback unit can customize the content of feedback based on the user's current health status. For example, if the user's current health status is good, the feedback unit will provide feedback to help maintain it. If the user's current health status is deteriorating, the feedback unit can also provide specific feedback for improvement. The feedback unit can also adjust the content of feedback according to the user's current health status. This allows for the provision of more appropriate feedback by customizing the content of feedback based on the user's current health status.

[0052] The feedback unit can provide optimal feedback by considering the user's geographical location. For example, if the user is at high altitude, the feedback unit can provide altitude-related health feedback. If the user is in an urban area, the feedback unit can also provide health feedback related to environmental pollution. If the user is indoors, the feedback unit can also provide health feedback related to the indoor environment. In this way, by considering the user's geographical location, the feedback unit can provide optimal feedback.

[0053] The feedback unit can analyze a user's social media activity and suggest appropriate feedback. For example, if a user is experiencing stress on social media, the feedback unit can provide feedback on stress reduction. If a user is relaxing on social media, the feedback unit can also provide feedback on maintaining health. If a user is posting about exercise on social media, the feedback unit can also provide exercise-related feedback. In this way, by analyzing a user's social media activity, it can provide relevant feedback.

[0054] The detection unit can improve the accuracy of blood glucose level fluctuations by referring to the user's past health data. For example, the detection unit identifies fluctuation patterns based on the user's past blood glucose data. The detection unit can also improve the accuracy of detection by referring to the user's past health data. The detection unit can also analyze the user's past health data and select the optimal detection method. As a result, the accuracy of blood glucose level fluctuation detection can be improved by referring to the user's past health data.

[0055] The detection unit can customize its detection method based on the user's current activity level when detecting fluctuations in blood glucose levels. For example, if the user is exercising, the detection unit can detect blood glucose fluctuations related to exercise. If the user is resting, the detection unit can also detect blood glucose fluctuations related to relaxation. If the user is outdoors, the detection unit can also detect blood glucose fluctuations while considering environmental data. This allows for more accurate detection by customizing the detection method based on the user's current activity level.

[0056] The detection unit can select the optimal detection method by considering the user's geographical location when detecting fluctuations in blood glucose levels. For example, if the user is at high altitude, the detection unit can detect fluctuations in blood glucose levels related to altitude. If the user is in an urban area, the detection unit can also detect fluctuations in blood glucose levels related to environmental pollution. If the user is indoors, the detection unit can also detect fluctuations in blood glucose levels related to the indoor environment. This allows the system to select the optimal detection method by considering the user's geographical location.

[0057] The detection unit can improve the accuracy of blood glucose level fluctuations by analyzing the user's social media activity. For example, if the user is experiencing stress on social media, the detection unit can detect blood glucose level fluctuations related to stress. If the user is relaxing on social media, the detection unit can also detect blood glucose level fluctuations related to relaxation. If the user is posting about exercise on social media, the detection unit can also detect blood glucose level fluctuations related to exercise. In this way, the accuracy of blood glucose level fluctuation detection can be improved by analyzing the user's social media activity.

[0058] The guidance system can suggest the most suitable restaurant by referring to the user's past dining history. For example, it can suggest a restaurant that matches the user's preferences based on their past dining history. The guidance system can also suggest restaurants that offer healthy meals based on the user's past dining history. The guidance system can also analyze the user's past dining history and select the most suitable restaurant. In this way, it can suggest the most suitable restaurant by referring to the user's past dining history.

[0059] The guidance system can customize the restaurant recommendations based on the user's current health condition. For example, if the user's current health condition is good, the guidance system will suggest restaurants suitable for maintaining good health. If the user's current health condition is deteriorating, the guidance system can also suggest restaurants suitable for improvement. The guidance system can also adjust the restaurant recommendations according to the user's current health condition. This allows for more appropriate recommendations by customizing the guidance based on the user's current health condition.

[0060] The guidance system can suggest the most suitable restaurant when providing restaurant recommendations, taking into account the user's geographical location. For example, if the user is at a high altitude, the guidance system can suggest a restaurant appropriate for that altitude. If the user is in an urban area, the guidance system can also suggest a restaurant that takes environmental pollution into consideration. If the user is indoors, the guidance system can also suggest a restaurant appropriate for the indoor environment. In this way, the system can suggest the most suitable restaurant by taking the user's geographical location into account.

[0061] The guidance department can analyze a user's social media activity when recommending restaurants to select a suitable restaurant. For example, if a user is experiencing stress on social media, the guidance department can suggest a restaurant suitable for stress reduction. If a user is relaxing on social media, the guidance department can also suggest a restaurant suitable for maintaining health. If a user is posting about exercise on social media, the guidance department can also suggest a restaurant suitable for a post-exercise meal. In this way, by analyzing a user's social media activity, relevant restaurants can be suggested.

[0062] The tracking unit can improve tracking accuracy by referring to the user's past lifestyle data when tracking lifestyle habits. For example, the tracking unit identifies tracking patterns based on the user's past lifestyle data. The tracking unit can also improve tracking accuracy by referring to the user's past lifestyle data. The tracking unit can also analyze the user's past lifestyle data and select the optimal tracking method. This allows for improved tracking accuracy by referring to the user's past lifestyle data.

[0063] The tracking unit can customize its tracking method based on the user's current activity level when tracking lifestyle habits. For example, if the user is exercising, the tracking unit will track lifestyle habits related to exercise. If the user is resting, the tracking unit can also track lifestyle habits related to relaxation. If the user is outdoors, the tracking unit can also track lifestyle habits while considering environmental data. This allows for more appropriate tracking by customizing the tracking method based on the user's current activity level.

[0064] The tracking unit can select the optimal tracking method when tracking lifestyle habits, taking into account the user's geographical location. For example, if the user is at high altitude, the tracking unit can track lifestyle habits related to altitude. If the user is in an urban area, the tracking unit can also track lifestyle habits related to environmental pollution. If the user is indoors, the tracking unit can also track lifestyle habits related to the indoor environment. This allows the system to select the optimal tracking method by considering the user's geographical location.

[0065] The tracking unit can improve the accuracy of tracking by analyzing the user's social media activity when tracking lifestyle habits. For example, if the user is experiencing stress on social media, the tracking unit can track lifestyle habits related to stress. If the user is relaxing on social media, the tracking unit can also track lifestyle habits related to relaxation. If the user is posting about exercise on social media, the tracking unit can also track lifestyle habits related to exercise. In this way, the accuracy of tracking can be improved by analyzing the user's social media activity.

[0066] The improvement unit can provide optimal improvement suggestions by referring to the user's past lifestyle data when suggesting lifestyle improvements. For example, the improvement unit can provide the most effective improvement suggestions based on the user's past lifestyle data. The improvement unit can also provide improvement suggestions for specific health conditions based on the user's past lifestyle data. The improvement unit can also customize the content of improvement suggestions based on the user's past lifestyle data. This allows the unit to provide optimal improvement suggestions by referring to the user's past lifestyle data.

[0067] The improvement unit can customize the content of lifestyle improvement suggestions based on the user's current health condition. For example, if the user's current health condition is good, the improvement unit will provide suggestions for maintaining that condition. If the user's current health condition is deteriorating, the improvement unit can also provide specific suggestions for improvement. The improvement unit can also adjust the content of the improvement suggestions according to the user's current health condition. This allows for more appropriate suggestions by customizing the content of the suggestions based on the user's current health condition.

[0068] The improvement unit can provide optimal suggestions for lifestyle improvements by considering the user's geographical location. For example, if the user is at a high altitude, the improvement unit can provide improvement suggestions related to altitude. If the user is in an urban area, the improvement unit can also provide improvement suggestions related to environmental pollution. If the user is indoors, the improvement unit can also provide improvement suggestions related to the indoor environment. In this way, by considering the user's geographical location, the improvement unit can provide optimal improvement suggestions.

[0069] The improvement department can analyze a user's social media activity and adjust the content of lifestyle improvement suggestions accordingly. For example, if a user is experiencing stress on social media, the improvement department can provide stress reduction suggestions. If a user is relaxing on social media, the improvement department can also provide health maintenance suggestions. If a user is posting about exercise on social media, the improvement department can also provide post-exercise improvement suggestions. In this way, by analyzing a user's social media activity, it can provide relevant improvement suggestions.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] Smart glasses are equipped with a data collection unit to gather user health data, an analysis unit to analyze the collected data, a suggestion unit to make health recommendations based on the analysis results, and a feedback unit to provide visual feedback. The data collection unit collects data on the user's diet, exercise, sleep, etc., and can also collect health data such as heart rate, blood pressure, and weight using wearable devices and smartphone apps. The analysis unit analyzes the data using statistical analysis and machine learning algorithms to understand the user's health status. The suggestion unit makes suggestions for dietary improvements and exercise recommendations based on the analysis results, and generates personalized health recommendations using AI. The feedback unit provides visual feedback in the form of graphs, charts, and animations based on the recommendations. In this way, by collecting, analyzing, suggesting, and providing feedback on the user's lifestyle habits and health data, it can support healthy lifestyle choices.

[0072] The smart glasses are equipped with a detection unit that detects fluctuations in blood glucose levels. The detection unit allows users to set the frequency of blood glucose measurement and the threshold for fluctuations, and uses AI to detect blood glucose fluctuations in real time. For example, it can notify the user when a blood glucose fluctuation is detected. This allows the system to understand the user's health status by detecting blood glucose fluctuations and provide appropriate suggestions.

[0073] The smart glasses feature a guidance unit that directs users to restaurants that recommend low-carb meals. The guidance unit selects restaurants based on carbohydrate content and recommended foods, and uses AI to guide users to suitable restaurants. For example, it can suggest restaurants based on the user's current location and dietary preferences. This allows the system to support users' healthy eating habits by guiding them to restaurants that recommend low-carb meals.

[0074] Smart glasses are equipped with a tracking unit that automatically tracks lifestyle habits. The tracking unit allows users to configure the sensors used and the types of data to track, and uses AI to automatically track the user's lifestyle habits. For example, it can track the user's exercise level and sleep duration in real time. This automatic tracking of lifestyle habits makes health management more efficient.

[0075] Smart glasses are equipped with an improvement unit that offers suggestions for improving lifestyle habits. This unit can recommend improvements to diet and exercise, and use AI to provide personalized suggestions tailored to the user. For example, it can provide individualized suggestions based on the user's health condition and lifestyle habits. This allows for improvements in the user's health through lifestyle improvements.

[0076] The data collection unit can analyze a user's past health data and select the optimal collection method. For example, it can identify the most effective collection time based on the user's past health data. It can also select a specific data collection method (e.g., wearable device, smartphone app) based on the user's past health data. Furthermore, it can optimize the collection frequency based on the user's past health data. This allows for efficient data collection by selecting the optimal collection method through analysis of the user's past health data.

[0077] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, it can prioritize collecting data related to exercise. If the user is resting, it can also collect data related to relaxation. If the user is outdoors, it can collect health data while considering environmental data (e.g., temperature, humidity). This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, it can collect altitude-related health data. If the user is in an urban area, it can also collect data related to environmental pollution. If the user is indoors, it can also collect data related to the indoor environment. In this way, by considering the user's geographical location, it is possible to collect highly relevant health data.

[0079] The following briefly describes the processing flow for example form 1.

[0080] Step 1: The data collection unit collects the user's lifestyle and health data. The data collection unit can collect data such as the user's diet, exercise, and sleep. The data collection unit can also collect data using wearable devices or smartphone apps. The data collection unit can collect health data such as the user's heart rate, blood pressure, and weight. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can use AI to analyze the data and understand the user's health status. Step 3: The proposal unit makes health recommendations based on the analysis results obtained by the analysis unit. The proposal unit can, for example, suggest dietary improvements or recommend exercise. The proposal unit can also generate personalized health recommendations using AI. Step 4: The feedback unit provides visual feedback based on the content proposed by the proposal unit. The feedback unit can provide feedback in the form of graphs, charts, animations, etc. The feedback unit can use AI to provide appropriate feedback to the user.

[0081] (Example of form 2) The smart glasses according to an embodiment of the present invention are a system that analyzes a user's lifestyle and health data and provides real-time visual feedback. These smart glasses collect the user's lifestyle and health data, and AI analyzes it to understand the user's health status. Based on the analysis results, the AI ​​provides personalized health suggestions and visual feedback. For example, it can detect fluctuations in blood glucose levels and recommend restaurants offering low-carbohydrate meals. It also automatically tracks lifestyle habits and suggests improvements, streamlining the user's health management. This is expected to lead to improved health, reduced medical costs, and prevention of lifestyle-related diseases. Furthermore, the integration of wearable devices and AI creates a sustainable health support system utilizing biometric data. The target audience is health-conscious individuals, those who want to prevent lifestyle-related diseases, and those who want to simplify their daily health management. Generative AI is used to analyze health data and generate personalized feedback. The market size is the wearable device and health management market, with increasing healthcare needs and technological advancements creating market opportunities. With these innovative smart glasses, we aim to revolutionize individual health management and realize a healthier society. This allows smart glasses to monitor the user's health in real time and provide appropriate feedback.

[0082] The smart glasses according to this embodiment include a data collection unit, an analysis unit, a suggestion unit, and a feedback unit. The data collection unit collects the user's lifestyle and health data. For example, the data collection unit can collect data such as the user's diet, exercise, and sleep. The data collection unit can also collect data using a wearable device or a smartphone application. For example, the data collection unit can collect health data such as the user's heart rate, blood pressure, and weight. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. The analysis unit can analyze the data using AI to understand the user's health status. The suggestion unit makes health suggestions based on the analysis results obtained by the analysis unit. For example, the suggestion unit can make suggestions for dietary improvements or recommend exercise. The suggestion unit can generate personalized health suggestions using AI. The feedback unit provides visual feedback based on the content suggested by the suggestion unit. For example, the feedback unit can provide feedback in the form of graphs, charts, animations, etc. The feedback unit can provide appropriate feedback to the user using AI. As a result, the smart glasses according to this embodiment can support healthy lifestyle choices by collecting, analyzing, suggesting, and providing feedback on the user's lifestyle and health data.

[0083] The data collection unit collects users' lifestyle and health data. Specifically, it can collect data on users' diet, exercise, sleep, and other aspects of their lives. The data collection unit can also collect data using wearable devices and smartphone apps. For example, a wearable device can monitor the user's health data, such as heart rate, blood pressure, and weight, in real time and transmit it to a smartphone app. This allows for continuous monitoring of the user's health status. Furthermore, the data collection unit has a function to automatically analyze calories and nutrients by taking photos of meals to record the user's diet. For exercise data, it accurately records the user's exercise volume and distance traveled using a pedometer and GPS function. For sleep data, it measures the user's sleep duration and sleep quality using sensors on wearable devices. This data is stored on a cloud server and later used by the analysis unit. To protect user privacy, the data collection unit encrypts and anonymizes data and manages it securely. In addition, the data collection unit collects data only with the user's consent, and users can stop data collection at any time if they wish. This allows the data collection unit to efficiently and securely collect users' lifestyle and health data, improving the overall system performance.

[0084] The analysis department analyzes the data collected by the data collection department. Specifically, it can analyze data using statistical analysis and machine learning algorithms. For example, statistical analysis calculates the mean and standard deviation of users' health data and detects outliers. By using machine learning algorithms, it can predict changes in users' health status and detect abnormal patterns early. AI-based data analysis analyzes users' health data from multiple perspectives and assesses health risks. For example, it analyzes heart rate and blood pressure data to assess stress levels and cardiovascular risk. It also analyzes dietary data to detect imbalances in nutrition. By analyzing exercise data, it understands users' exercise habits and warns of insufficient or excessive exercise. By analyzing sleep data, it evaluates the quality of users' sleep and suggests areas for improvement. The analysis department comprehensively analyzes this data to gain a holistic understanding of users' health status. Furthermore, by comparing past data and data from other users, the analysis department can grasp trends in users' health status and support long-term health management. In this way, the analysis department can accurately understand users' health status and provide the foundational data for making appropriate health suggestions.

[0085] The Proposal Department provides health recommendations based on the analysis results obtained by the Analysis Department. Specifically, it can offer suggestions for dietary improvements and exercise recommendations. For example, in dietary improvement suggestions, it identifies areas for improvement in nutritional balance and proposes specific meal menus based on the user's dietary data. In exercise recommendations, it suggests appropriate exercise volume and type based on the user's exercise data. The Proposal Department can generate personalized health recommendations using AI. The AI ​​learns the user's health data and lifestyle habits and provides suggestions tailored to the user's needs. For example, if the user wants to lose weight, it will suggest calorie restrictions and exercise plans. If the user is feeling stressed, it will suggest relaxation methods and stress management techniques. The Proposal Department can collect user feedback and continuously improve its recommendations. For example, it can record the results of actions taken by the user following the recommendations and evaluate their effectiveness. The Proposal Department can flexibly adjust its recommendations according to changes in the user's health condition and lifestyle habits. This allows the Proposal Department to provide users with optimal health recommendations and support them in making healthy lifestyle choices.

[0086] The Feedback Department provides visual feedback based on the suggestions made by the Proposal Department. Specifically, feedback can be provided in the form of graphs, charts, and animations. For example, it can graph the user's health data to visually show changes in their health status. It can use charts to display trends in dietary balance and exercise levels. It can use animations to clearly explain how to implement health suggestions. The Feedback Department can use AI to provide appropriate feedback to the user. The AI ​​learns the user's reactions and behaviors and optimizes the content and format of the feedback. For example, if the user prefers visual feedback, it will make extensive use of graphs and charts. If the user requests specific action instructions, it will provide animations and step-by-step guides. The Feedback Department can visually show achievement goals and progress to increase the user's motivation. For example, it can display progress toward goal achievement with a bar graph to give a sense of accomplishment. The Feedback Department can collect user feedback and continuously improve the accuracy and effectiveness of the feedback content. This allows the Feedback Department to provide effective visual feedback to users and support them in making healthy lifestyle choices.

[0087] The smart glasses are equipped with a detection unit that detects fluctuations in blood glucose levels. The detection unit allows for settings such as the frequency of blood glucose measurement and the threshold for fluctuations. The detection unit can use AI to detect blood glucose fluctuations in real time. For example, the detection unit can notify the user when it detects a change in blood glucose levels. This allows the system to understand the user's health status by detecting blood glucose fluctuations and provide appropriate suggestions.

[0088] The smart glasses feature a guidance unit that directs users to restaurants that recommend low-carbohydrate meals. The guidance unit can select restaurants based, for example, on carbohydrate content and recommended foods. It can also use AI to recommend restaurants suitable for the user. For example, it can suggest restaurants based on the user's current location and dietary preferences. This allows the smart glasses to support users' healthy eating habits by guiding them to restaurants that recommend low-carbohydrate meals.

[0089] Smart glasses are equipped with a tracking unit that automatically tracks lifestyle habits. The tracking unit allows users to configure, for example, the sensors to be used and the types of data to be tracked. The tracking unit can automatically track the user's lifestyle habits using AI. For example, the tracking unit can track the user's exercise level and sleep duration in real time. This allows for more efficient health management by automatically tracking lifestyle habits.

[0090] Smart glasses are equipped with an improvement unit that offers suggestions for improving lifestyle habits. This unit can, for example, recommend improvements to diet and exercise. The improvement unit can use AI to provide personalized improvement suggestions tailored to the user. For example, it can provide personalized suggestions based on the user's health condition and lifestyle habits. This allows for improvements in the user's health condition through lifestyle improvements.

[0091] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect health data during times when the user is relaxed. If the user is relaxed, the data collection unit can also collect health data during times when the user is active. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected in a short amount of time. This allows for more appropriate data collection by adjusting the timing of health data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the most effective time of day for data collection based on the user's past health data. The data collection unit can also select a specific data collection method (e.g., wearable device, smartphone app) based on the user's past health data. The data collection unit can also optimize the collection frequency based on the user's past health data. This allows for efficient data collection by selecting the optimal collection method through analysis of the user's past health data.

[0093] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, the unit will prioritize collecting data related to exercise. If the user is resting, the unit can also collect data related to relaxation. If the user is outdoors, the unit can also collect health data while considering environmental data (e.g., temperature, humidity). This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment.

[0094] The data collection unit can estimate the user's emotions and prioritize the health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress levels. If the user is relaxed, the data collection unit can also prioritize collecting data related to relaxation. If the user is in a hurry, the data collection unit can also prioritize collecting data that can be collected in a short amount of time. This allows for the priority collection of important data by prioritizing health data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit can collect altitude-related health data. If the user is in an urban area, the data collection unit can also collect data related to environmental pollution. If the user is indoors, the data collection unit can also collect data related to the indoor environment. In this way, by considering the user's geographical location, highly relevant health data can be collected.

[0096] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the unit can collect data related to their stress level. If a user is relaxing on social media, the unit can also collect data related to their relaxation state. If a user is posting about exercise on social media, the unit can also collect data related to exercise. In this way, relevant health data can be collected by analyzing a user's social media activity.

[0097] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit will perform a detailed analysis on important health data, while performing a simplified analysis on general health data. The analysis unit can also adjust the level of detail of the analysis according to the user's health status. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the health data.

[0099] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, it can apply a blood glucose variability analysis algorithm to blood glucose data. It can also apply a heart rate variability analysis algorithm to heart rate data. It can also apply a sleep quality evaluation algorithm to sleep data. By applying different analysis algorithms depending on the category of health data, more accurate analysis becomes possible.

[0100] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with detailed explanations. If the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The analysis department can prioritize analyses based on when health data was collected. For example, it might prioritize analyzing recently collected health data. It can also prioritize analyzing data collected during a specific period. The analysis department can also adjust the analysis priority according to the user's health status. This allows for efficient data analysis by prioritizing analyses based on when health data was collected.

[0102] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also adjust the order of analysis according to the user's health status. The analysis unit can also determine the order of analysis based on specific health data categories. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of health data.

[0103] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion function may also provide detailed suggestions. If the user is in a hurry, the suggestion function may also provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The proposal department can adjust the level of detail in its proposals based on the importance of each health suggestion. For example, it can provide detailed suggestions for important health suggestions, while providing concise suggestions for general health suggestions. The proposal department can also adjust the level of detail according to the user's health status. This allows for more efficient proposals by adjusting the level of detail based on the importance of each health suggestion.

[0105] The suggestion function can apply different suggestion algorithms depending on the category of the health suggestion. For example, for a meal suggestion, it can apply a suggestion algorithm that considers the nutritional balance of the meal. For an exercise suggestion, it can also apply a suggestion algorithm that considers the effects of exercise. For a sleep suggestion, it can also apply a suggestion algorithm that improves sleep quality. By applying different suggestion algorithms depending on the category of the health suggestion, more appropriate suggestions can be made.

[0106] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function may provide longer suggestions with more detailed explanations. If the user is excited, the suggestion function may also provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. 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.

[0107] The proposal department can prioritize proposals based on when health data was collected. For example, the proposal department can make proposals based on recently collected health data. The proposal department can also make proposals based on data collected during a specific period. The proposal department can also adjust the priority of proposals according to the user's health status. This allows for more efficient proposals by prioritizing proposals based on when health data was collected.

[0108] The suggestion unit can adjust the order of suggestions based on the relevance of health data during the suggestion process. For example, the suggestion unit makes suggestions based on highly relevant data. The suggestion unit can also adjust the order of suggestions according to the user's health status. The suggestion unit can also determine the order of suggestions based on specific health data categories. This allows for more efficient suggestions by adjusting the order of suggestions based on the relevance of health data.

[0109] The feedback unit can estimate the user's emotions and adjust how the feedback is displayed based on those emotions. For example, if the user is stressed, the feedback unit provides simple, easy-to-understand feedback. If the user is relaxed, the feedback unit can also provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise, to-the-point feedback. This allows for more appropriate feedback to be provided by adjusting how the feedback is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The feedback unit can provide optimal feedback by referencing the user's past health data during the feedback process. For example, the feedback unit can provide the most effective feedback based on the user's past health data. The feedback unit can also provide feedback on specific health conditions based on the user's past health data. Furthermore, the feedback unit can customize the content of the feedback based on the user's past health data. This allows the system to provide optimal feedback by referencing the user's past health data.

[0111] The feedback unit can customize the content of feedback based on the user's current health status. For example, if the user's current health status is good, the feedback unit will provide feedback to help maintain it. If the user's current health status is deteriorating, the feedback unit can also provide specific feedback for improvement. The feedback unit can also adjust the content of feedback according to the user's current health status. This allows for the provision of more appropriate feedback by customizing the content of feedback based on the user's current health status.

[0112] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit will prioritize providing stress-reducing feedback. If the user is relaxed, the feedback unit may also prioritize providing health-maintaining feedback. If the user is in a hurry, the feedback unit may also prioritize providing quick, actionable feedback. This allows for more appropriate feedback to be provided by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The feedback unit can provide optimal feedback by considering the user's geographical location. For example, if the user is at high altitude, the feedback unit can provide altitude-related health feedback. If the user is in an urban area, the feedback unit can also provide health feedback related to environmental pollution. If the user is indoors, the feedback unit can also provide health feedback related to the indoor environment. In this way, by considering the user's geographical location, the feedback unit can provide optimal feedback.

[0114] The feedback unit can analyze a user's social media activity and suggest appropriate feedback. For example, if a user is experiencing stress on social media, the feedback unit can provide feedback on stress reduction. If a user is relaxing on social media, the feedback unit can also provide feedback on maintaining health. If a user is posting about exercise on social media, the feedback unit can also provide exercise-related feedback. In this way, by analyzing a user's social media activity, it can provide relevant feedback.

[0115] The detection unit can estimate the user's emotions and adjust the timing of blood glucose fluctuation detection based on the estimated emotions. For example, if the user is stressed, the detection unit will frequently detect blood glucose fluctuations. If the user is relaxed, the detection unit can also detect blood glucose fluctuations at normal intervals. If the user is in a hurry, the detection unit can also detect blood glucose fluctuations in a short time. By adjusting the timing of blood glucose fluctuation detection according to the user's emotions, detection can be performed at a more appropriate time. 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.

[0116] The detection unit can improve the accuracy of blood glucose level fluctuations by referring to the user's past health data. For example, the detection unit identifies fluctuation patterns based on the user's past blood glucose data. The detection unit can also improve the accuracy of detection by referring to the user's past health data. The detection unit can also analyze the user's past health data and select the optimal detection method. As a result, the accuracy of blood glucose level fluctuation detection can be improved by referring to the user's past health data.

[0117] The detection unit can customize its detection method based on the user's current activity level when detecting fluctuations in blood glucose levels. For example, if the user is exercising, the detection unit can detect blood glucose fluctuations related to exercise. If the user is resting, the detection unit can also detect blood glucose fluctuations related to relaxation. If the user is outdoors, the detection unit can also detect blood glucose fluctuations while considering environmental data. This allows for more accurate detection by customizing the detection method based on the user's current activity level.

[0118] The detection unit can estimate the user's emotions and determine the priority of blood glucose fluctuation detection based on the estimated emotions. For example, if the user is stressed, the detection unit will prioritize blood glucose fluctuation detection. If the user is relaxed, the detection unit can also perform blood glucose fluctuation detection with the normal priority. If the user is in a hurry, the detection unit can also perform blood glucose fluctuation detection in a short time. This allows for more appropriate timing of detection by determining the priority of blood glucose fluctuation detection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The detection unit can select the optimal detection method by considering the user's geographical location when detecting fluctuations in blood glucose levels. For example, if the user is at high altitude, the detection unit can detect fluctuations in blood glucose levels related to altitude. If the user is in an urban area, the detection unit can also detect fluctuations in blood glucose levels related to environmental pollution. If the user is indoors, the detection unit can also detect fluctuations in blood glucose levels related to the indoor environment. This allows the system to select the optimal detection method by considering the user's geographical location.

[0120] The detection unit can improve the accuracy of blood glucose level fluctuations by analyzing the user's social media activity. For example, if the user is experiencing stress on social media, the detection unit can detect blood glucose level fluctuations related to stress. If the user is relaxing on social media, the detection unit can also detect blood glucose level fluctuations related to relaxation. If the user is posting about exercise on social media, the detection unit can also detect blood glucose level fluctuations related to exercise. In this way, the accuracy of blood glucose level fluctuation detection can be improved by analyzing the user's social media activity.

[0121] The guidance system can estimate the user's emotions and adjust the way restaurant recommendations are presented based on those emotions. For example, if the user is stressed, the guidance system can provide a simple and easy-to-read restaurant recommendation. If the user is relaxed, the guidance system can also provide a detailed recommendation. If the user is in a hurry, the guidance system can provide a concise recommendation that gets straight to the point. By adjusting the way restaurant recommendations are presented according to the user's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The guidance system can suggest the most suitable restaurant by referring to the user's past dining history. For example, it can suggest a restaurant that matches the user's preferences based on their past dining history. The guidance system can also suggest restaurants that offer healthy meals based on the user's past dining history. The guidance system can also analyze the user's past dining history and select the most suitable restaurant. In this way, it can suggest the most suitable restaurant by referring to the user's past dining history.

[0123] The guidance system can customize the restaurant recommendations based on the user's current health condition. For example, if the user's current health condition is good, the guidance system will suggest restaurants suitable for maintaining good health. If the user's current health condition is deteriorating, the guidance system can also suggest restaurants suitable for improvement. The guidance system can also adjust the restaurant recommendations according to the user's current health condition. This allows for more appropriate recommendations by customizing the guidance based on the user's current health condition.

[0124] The guidance system can estimate the user's emotions and prioritize restaurant recommendations based on those emotions. For example, if the user is feeling stressed, the guidance system will prioritize restaurants suitable for stress reduction. If the user is relaxed, the guidance system can also prioritize restaurants suitable for maintaining health. If the user is in a hurry, the guidance system can also prioritize restaurants where meals can be eaten quickly. This allows for more appropriate guidance by prioritizing restaurant recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The guidance system can suggest the most suitable restaurant when providing restaurant recommendations, taking into account the user's geographical location. For example, if the user is at a high altitude, the guidance system can suggest a restaurant appropriate for that altitude. If the user is in an urban area, the guidance system can also suggest a restaurant that takes environmental pollution into consideration. If the user is indoors, the guidance system can also suggest a restaurant appropriate for the indoor environment. In this way, the system can suggest the most suitable restaurant by taking the user's geographical location into account.

[0126] The guidance department can analyze a user's social media activity when recommending restaurants to select a suitable restaurant. For example, if a user is experiencing stress on social media, the guidance department can suggest a restaurant suitable for stress reduction. If a user is relaxing on social media, the guidance department can also suggest a restaurant suitable for maintaining health. If a user is posting about exercise on social media, the guidance department can also suggest a restaurant suitable for a post-exercise meal. In this way, by analyzing a user's social media activity, relevant restaurants can be suggested.

[0127] The tracking unit can estimate the user's emotions and adjust the lifestyle tracking method based on the estimated emotions. For example, if the user is stressed, the tracking unit will prioritize tracking lifestyle habits related to stress reduction. If the user is relaxed, the tracking unit can also track lifestyle habits related to maintaining health. If the user is in a hurry, the tracking unit can also prioritize tracking lifestyle habits that can be tracked in a short amount of time. This allows for more appropriate tracking by adjusting the lifestyle tracking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The tracking unit can improve tracking accuracy by referring to the user's past lifestyle data when tracking lifestyle habits. For example, the tracking unit identifies tracking patterns based on the user's past lifestyle data. The tracking unit can also improve tracking accuracy by referring to the user's past lifestyle data. The tracking unit can also analyze the user's past lifestyle data and select the optimal tracking method. This allows for improved tracking accuracy by referring to the user's past lifestyle data.

[0129] The tracking unit can customize its tracking method based on the user's current activity level when tracking lifestyle habits. For example, if the user is exercising, the tracking unit will track lifestyle habits related to exercise. If the user is resting, the tracking unit can also track lifestyle habits related to relaxation. If the user is outdoors, the tracking unit can also track lifestyle habits while considering environmental data. This allows for more appropriate tracking by customizing the tracking method based on the user's current activity level.

[0130] The tracking unit can estimate the user's emotions and determine the priority of lifestyle habit tracking based on the estimated emotions. For example, if the user is stressed, the tracking unit will prioritize tracking lifestyle habits related to stress reduction. If the user is relaxed, the tracking unit can also prioritize tracking lifestyle habits related to maintaining health. If the user is in a hurry, the tracking unit can also prioritize tracking lifestyle habits that can be tracked in a short time. This allows for more appropriate tracking by determining the priority of lifestyle habit tracking according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] The tracking unit can select the optimal tracking method when tracking lifestyle habits, taking into account the user's geographical location. For example, if the user is at high altitude, the tracking unit can track lifestyle habits related to altitude. If the user is in an urban area, the tracking unit can also track lifestyle habits related to environmental pollution. If the user is indoors, the tracking unit can also track lifestyle habits related to the indoor environment. This allows the system to select the optimal tracking method by considering the user's geographical location.

[0132] The tracking unit can improve the accuracy of tracking by analyzing the user's social media activity when tracking lifestyle habits. For example, if the user is experiencing stress on social media, the tracking unit can track lifestyle habits related to stress. If the user is relaxing on social media, the tracking unit can also track lifestyle habits related to relaxation. If the user is posting about exercise on social media, the tracking unit can also track lifestyle habits related to exercise. In this way, the accuracy of tracking can be improved by analyzing the user's social media activity.

[0133] The improvement unit can estimate the user's emotions and adjust the way lifestyle improvement suggestions are presented based on those emotions. For example, if the user is stressed, the improvement unit will provide simple and highly visible improvement suggestions. If the user is relaxed, the improvement unit can also provide detailed improvement suggestions. If the user is in a hurry, the improvement unit can also provide concise suggestions that get straight to the point. By adjusting the way lifestyle improvement suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The improvement unit can provide optimal improvement suggestions by referring to the user's past lifestyle data when suggesting lifestyle improvements. For example, the improvement unit can provide the most effective improvement suggestions based on the user's past lifestyle data. The improvement unit can also provide improvement suggestions for specific health conditions based on the user's past lifestyle data. The improvement unit can also customize the content of improvement suggestions based on the user's past lifestyle data. This allows the unit to provide optimal improvement suggestions by referring to the user's past lifestyle data.

[0135] The improvement unit can customize the content of lifestyle improvement suggestions based on the user's current health condition. For example, if the user's current health condition is good, the improvement unit will provide suggestions for maintaining that condition. If the user's current health condition is deteriorating, the improvement unit can also provide specific suggestions for improvement. The improvement unit can also adjust the content of the improvement suggestions according to the user's current health condition. This allows for more appropriate suggestions by customizing the content of the suggestions based on the user's current health condition.

[0136] The improvement unit can estimate the user's emotions and prioritize lifestyle improvement suggestions based on those emotions. For example, if the user is feeling stressed, the improvement unit will prioritize stress reduction suggestions. If the user is relaxed, the improvement unit can also prioritize health maintenance suggestions. If the user is in a hurry, the improvement unit can also prioritize suggestions that can be implemented quickly. By prioritizing lifestyle improvement suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0137] The improvement unit can provide optimal suggestions for lifestyle improvements by considering the user's geographical location. For example, if the user is at a high altitude, the improvement unit can provide improvement suggestions related to altitude. If the user is in an urban area, the improvement unit can also provide improvement suggestions related to environmental pollution. If the user is indoors, the improvement unit can also provide improvement suggestions related to the indoor environment. In this way, by considering the user's geographical location, the improvement unit can provide optimal improvement suggestions.

[0138] The improvement department can analyze a user's social media activity and adjust the content of lifestyle improvement suggestions accordingly. For example, if a user is experiencing stress on social media, the improvement department can provide stress reduction suggestions. If a user is relaxing on social media, the improvement department can also provide health maintenance suggestions. If a user is posting about exercise on social media, the improvement department can also provide post-exercise improvement suggestions. In this way, by analyzing a user's social media activity, it can provide relevant improvement suggestions.

[0139] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0140] Smart glasses are equipped with a data collection unit to gather user health data, an analysis unit to analyze the collected data, a suggestion unit to make health recommendations based on the analysis results, and a feedback unit to provide visual feedback. The data collection unit collects data on the user's diet, exercise, sleep, etc., and can also collect health data such as heart rate, blood pressure, and weight using wearable devices and smartphone apps. The analysis unit analyzes the data using statistical analysis and machine learning algorithms to understand the user's health status. The suggestion unit makes suggestions for dietary improvements and exercise recommendations based on the analysis results, and generates personalized health recommendations using AI. The feedback unit provides visual feedback in the form of graphs, charts, and animations based on the recommendations. In this way, by collecting, analyzing, suggesting, and providing feedback on the user's lifestyle habits and health data, it can support healthy lifestyle choices.

[0141] The smart glasses are equipped with a detection unit that detects fluctuations in blood glucose levels. The detection unit allows users to set the frequency of blood glucose measurement and the threshold for fluctuations, and uses AI to detect blood glucose fluctuations in real time. For example, it can notify the user when a blood glucose fluctuation is detected. This allows the system to understand the user's health status by detecting blood glucose fluctuations and provide appropriate suggestions.

[0142] The smart glasses feature a guidance unit that directs users to restaurants that recommend low-carb meals. The guidance unit selects restaurants based on carbohydrate content and recommended foods, and uses AI to guide users to suitable restaurants. For example, it can suggest restaurants based on the user's current location and dietary preferences. This allows the system to support users' healthy eating habits by guiding them to restaurants that recommend low-carb meals.

[0143] Smart glasses are equipped with a tracking unit that automatically tracks lifestyle habits. The tracking unit allows users to configure the sensors used and the types of data to track, and uses AI to automatically track the user's lifestyle habits. For example, it can track the user's exercise level and sleep duration in real time. This automatic tracking of lifestyle habits makes health management more efficient.

[0144] Smart glasses are equipped with an improvement unit that offers suggestions for improving lifestyle habits. This unit can recommend improvements to diet and exercise, and use AI to provide personalized suggestions tailored to the user. For example, it can provide individualized suggestions based on the user's health condition and lifestyle habits. This allows for improvements in the user's health through lifestyle improvements.

[0145] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, health data can be collected during times when they are relaxed. If the user is relaxed, health data can be collected during times when they are active. If the user is in a hurry, data that can be collected in a short time can be prioritized. This allows for more appropriate data collection by adjusting the timing of health data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI.

[0146] The data collection unit can analyze a user's past health data and select the optimal collection method. For example, it can identify the most effective collection time based on the user's past health data. It can also select a specific data collection method (e.g., wearable device, smartphone app) based on the user's past health data. Furthermore, it can optimize the collection frequency based on the user's past health data. This allows for efficient data collection by selecting the optimal collection method through analysis of the user's past health data.

[0147] The data collection unit can filter health data based on the user's current activity level and environment. For example, if the user is exercising, it can prioritize collecting data related to exercise. If the user is resting, it can also collect data related to relaxation. If the user is outdoors, it can collect health data while considering environmental data (e.g., temperature, humidity). This allows for the collection of more relevant data by filtering the data based on the user's current activity level and environment.

[0148] The data collection unit can estimate the user's emotions and prioritize the health data to collect based on those emotions. For example, if the user is stressed, it can prioritize collecting data related to stress levels. If the user is relaxed, it can prioritize collecting data related to relaxation. If the user is in a hurry, it can prioritize collecting data that can be collected in a short amount of time. This allows for the priority collection of important data by prioritizing health data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI.

[0149] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, it can collect altitude-related health data. If the user is in an urban area, it can also collect data related to environmental pollution. If the user is indoors, it can also collect data related to the indoor environment. In this way, by considering the user's geographical location, it is possible to collect highly relevant health data.

[0150] The following briefly describes the processing flow for example form 2.

[0151] Step 1: The data collection unit collects the user's lifestyle and health data. The data collection unit can collect data such as the user's diet, exercise, and sleep. The data collection unit can also collect data using wearable devices or smartphone apps. The data collection unit can collect health data such as the user's heart rate, blood pressure, and weight. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can use AI to analyze the data and understand the user's health status. Step 3: The proposal unit makes health recommendations based on the analysis results obtained by the analysis unit. The proposal unit can, for example, suggest dietary improvements or recommend exercise. The proposal unit can also generate personalized health recommendations using AI. Step 4: The feedback unit provides visual feedback based on the content proposed by the proposal unit. The feedback unit can provide feedback in the form of graphs, charts, animations, etc. The feedback unit can use AI to provide appropriate feedback to the user.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, feedback unit, detection unit, guidance unit, tracking unit, and improvement unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's lifestyle and health data using the sensors and applications of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to understand the user's health status. The suggestion unit generates personalized health suggestions using the specific processing unit 290 of the data processing unit 12. The feedback unit provides visual feedback using the display 40A of the smart device 14. The detection unit detects fluctuations in blood glucose levels in real time using the sensors of the smart device 14. The guidance unit guides the user to a suitable restaurant using the specific processing unit 290 of the data processing unit 12. The tracking unit automatically tracks the user's lifestyle using the sensors of the smart device 14. The improvement unit makes improvement suggestions suitable for the user using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, feedback unit, detection unit, guidance unit, tracking unit, and improvement unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's lifestyle and health data using the sensors and applications of the smart glasses 214. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12 to understand the user's health status. The suggestion unit generates personalized health suggestions by the identification processing unit 290 of the data processing unit 12. The feedback unit provides visual feedback using the display of the smart glasses 214. The detection unit detects fluctuations in blood glucose levels in real time using the sensors of the smart glasses 214. The guidance unit guides the user to a suitable restaurant by the identification processing unit 290 of the data processing unit 12. The tracking unit automatically tracks the user's lifestyle using the sensors of the smart glasses 214. The improvement unit makes improvement suggestions suitable for the user by the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, feedback unit, detection unit, guidance unit, tracking unit, and improvement unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's lifestyle and health data using the sensors and applications of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to understand the user's health status. The suggestion unit generates personalized health suggestions using the identification processing unit 290 of the data processing unit 12. The feedback unit provides visual feedback using the display of the headset terminal 314. The detection unit detects fluctuations in blood glucose levels in real time using the sensors of the headset terminal 314. The guidance unit guides the user to a suitable restaurant using the identification processing unit 290 of the data processing unit 12. The tracking unit automatically tracks the user's lifestyle using the sensors of the headset terminal 314. The improvement unit makes improvement suggestions suitable for the user using the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0188] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, feedback unit, detection unit, guidance unit, tracking unit, and improvement unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's lifestyle and health data using the sensors and applications of the robot 414. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12 to understand the user's health status. The suggestion unit generates personalized health suggestions by the identification processing unit 290 of the data processing unit 12. The feedback unit provides visual feedback using the display of the robot 414. The detection unit detects fluctuations in blood glucose levels in real time using the sensors of the robot 414. The guidance unit guides the user to a suitable restaurant by the identification processing unit 290 of the data processing unit 12. The tracking unit automatically tracks the user's lifestyle using the sensors of the robot 414. The improvement unit makes improvement suggestions suitable for the user by the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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."

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] (Note 1) A data collection unit that collects users' lifestyle habits and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that makes health recommendations based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that provides visual feedback based on the content proposed by the proposal unit. A system characterized by the following features. (Note 2) It is equipped with a detection unit that detects fluctuations in blood glucose levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a guide desk that directs customers to restaurants that recommend low-carb meals. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a tracking unit that automatically tracks lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 5) The company has an improvement department that provides suggestions for improving lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current activity level and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the health suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the health proposal. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when the health data will be collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, we refer to the user's past health data to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is 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 29) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The detection unit, The system estimates the user's emotions and adjusts the timing of blood glucose level fluctuation detection based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The detection unit, When detecting fluctuations in blood glucose levels, the system improves detection accuracy by referencing the user's past health data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The detection unit, When detecting fluctuations in blood glucose levels, the detection method is customized based on the user's current activity level. The system described in Appendix 2, characterized by the features described herein. (Note 33) The detection unit, The system estimates the user's emotions and determines the priority of detecting blood glucose fluctuations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The detection unit, When detecting fluctuations in blood glucose levels, the system selects the optimal detection method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The detection unit, When detecting fluctuations in blood glucose levels, the system analyzes the user's social media activity to improve the accuracy of the detection. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned guide section is The system estimates the user's emotions and adjusts the way restaurant recommendations are presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned guide section is When recommending restaurants, the system will suggest the most suitable restaurant based on the user's past dining history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned guide section is When recommending restaurants, customize the recommendations based on the user's current health status. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned guide section is The system estimates the user's emotions and prioritizes restaurant recommendations based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned guide section is When recommending restaurants, the system takes the user's geographical location into consideration to suggest the most suitable restaurant. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned guide section is When recommending restaurants, the system analyzes the user's social media activity to select the best restaurant. The system described in Appendix 3, characterized by the features described herein. (Appendix 42) The tracking unit estimates the user's emotion and adjusts the method of tracking the living habit based on the estimated user emotion The system according to Appendix 4, characterized in that (Appendix 43) The tracking unit improves the accuracy of tracking by referring to the user's past living data when tracking the living habit The system according to Appendix 4, characterized in that (Appendix 44) The tracking unit customizes the tracking method based on the user's current activity status when tracking the living habit The system according to Appendix 4, characterized in that (Appendix 45) The tracking unit estimates the user's emotion and determines the priority of tracking the living habit based on the estimated user emotion The system according to Appendix 4, characterized in that (Appendix 46) The tracking unit selects an optimal tracking method by considering the user's geographical location information when tracking the living habit The system according to Appendix 4, characterized in that (Appendix 47) The tracking unit improves the accuracy of tracking by analyzing the user's social media activities when tracking the living habit The system according to Appendix 4, characterized in that (Appendix 48) The improvement unit estimates the user's emotion and adjusts the expression method of the improvement proposal for the living habit based on the estimated user emotion The system according to Appendix 5, characterized in that (Appendix 49) The improvement unit makes an optimal improvement proposal by referring to the user's past living data when making an improvement proposal for the living habit The system described in Appendix 5, characterized by the features described herein. (Note 50) The aforementioned improvement unit is, When suggesting lifestyle improvements, the suggestions are customized based on the user's current health condition. The system described in Appendix 5, characterized by the features described herein. (Note 51) The aforementioned improvement unit is, The system estimates the user's emotions and prioritizes lifestyle improvement suggestions based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 52) The aforementioned improvement unit is, When suggesting improvements to lifestyle habits, the system takes the user's geographical location into consideration to provide the most optimal suggestions. The system described in Appendix 5, characterized by the features described herein. (Note 53) The aforementioned improvement unit is, When suggesting lifestyle improvements, we analyze the user's social media activity and adjust the content of the suggestions accordingly. The system described in Appendix 5, characterized by the features described herein. [Explanation of symbols]

[0224] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects users' lifestyle habits and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that makes health recommendations based on the analysis results obtained by the aforementioned analysis unit, The system includes a feedback unit that provides visual feedback based on the content proposed by the proposal unit. A system characterized by the following features.

2. It is equipped with a detection unit that detects fluctuations in blood glucose levels. The system according to feature 1.

3. It has a guide desk that directs customers to restaurants that recommend low-carb meals. The system according to feature 1.

4. It is equipped with a tracking unit that automatically tracks lifestyle habits. The system according to feature 1.

5. The company has an improvement department that provides suggestions for improving lifestyle habits. The system according to feature 1.

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

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

8. The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current activity level and environment. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the health data to collect based on those estimated emotions. The system according to feature 1.

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