system

Abstract

The system according to this embodiment aims to manage hormone balance in order to effectively support the mental and physical health of women in menopause. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a management unit, and a provision unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The management unit manages hormone balance based on the data analyzed by the analysis unit. The provision unit provides the user with the results managed by the management unit.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, hormone balance management for effectively supporting the physical and mental health of the menopausal generation has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to manage hormone balance in order to effectively support the physical and mental health of the menopausal generation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a management unit, and a data provision unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit. The management unit manages hormone balance based on the data analyzed by the analysis unit. The data provision unit provides the user with the results managed by the management unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage hormone balance to effectively support the mental and physical health of women in menopause. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health support system according to an embodiment of the present invention is a tool for supporting the mental and physical health of women in menopause. This health support system uses AI technology to help women achieve a mental and physical state that is not at the mercy of hormonal imbalances, and to live each day comfortably. The target audience includes women who are experiencing or about to experience menopause, those who need information related to menopause, and those who are suffering from symptoms. One of the challenges faced by these target audiences is that accurate information about menopause is not readily available, making it difficult to find appropriate ways to cope with symptoms and changes. To address this challenge, the system provides specialized medical consultations, learning content, exercise and meal plans for a healthy lifestyle, community forums for interaction among those experiencing menopause, and audio guides to promote relaxation, among other resources for menopausal women to obtain information. In terms of market size, there are billions of women worldwide who experience menopause, and many of them are seeking information and support during this period. There is a need for a platform that allows them to easily obtain the information they need, and generative AI technology, which provides solutions to the unique experiences and problems of women in menopause, shows the potential to meet this demand. Through this platform, we aim to provide support to empower menopausal women to take ownership of their own health, helping them manage menopausal symptoms and emotional fluctuations, and leading healthier and more fulfilling lives. In this way, the health support system can support the mental and physical health of the menopausal generation.

[0029] The health support system according to the embodiment comprises a data collection unit, an analysis unit, a management unit, and a data provision unit. The data collection unit collects user data. User data includes, but is not limited to, health data, behavioral data, and emotional data. The data collection unit collects user health data, for example, using sensors. The data collection unit can also collect user behavioral data through questionnaire surveys. Furthermore, the data collection unit may use facial recognition technology to collect user emotional data. For example, the data collection unit monitors the user's heart rate and activity level using a wearable device and collects health data. Questionnaire surveys are an effective means of collecting information about the user's daily activities and lifestyle. Facial recognition technology is used to analyze the user's facial expressions and estimate their emotional state. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes user health data using statistical analysis. The analysis unit can also analyze user behavioral data using machine learning algorithms. Furthermore, the analysis unit can also use natural language processing technology to analyze emotional data. For example, the analysis unit statistically analyzes the user's health data to understand trends in their health status. Machine learning algorithms are used to learn the user's behavior patterns and predict future behavior. Natural language processing technology is used to analyze the user's emotional data and detect changes in emotions. The management unit manages hormone balance based on the data analyzed by the analysis unit. Hormone balance management is carried out, for example, by measuring and managing hormone levels, but is not limited to such examples. For example, the management unit measures hormone levels and proposes appropriate management measures. The management unit can also manage hormone balance through lifestyle improvements. The management unit can also adjust hormone balance using hormone therapy. For example, the management unit performs regular hormone level measurements and takes appropriate measures if abnormalities are detected. Lifestyle improvements are an effective means of regulating hormone balance through a review of diet and exercise.Hormone therapy is a treatment method performed under the guidance of a physician and is used to adjust hormone balance. The provision unit provides the user with the results managed by the management unit. The provision is carried out, for example, through app notifications or email, but is not limited to such examples. For example, the provision unit informs the user of changes in their health status through app notifications. The provision unit can also provide the user with health management advice through email. The provision unit can also provide the user with health information through a web portal. For example, the provision unit notifies the user of changes in their health status in real time using app notifications. Email is an effective means of providing the user with detailed health management advice. The web portal functions as a platform that the user can access and check health information. In this way, the health support system according to the embodiment can support the mental and physical health of the menopausal generation. Some or all of the processing described above in the provision unit may be carried out using, for example, AI, or not using AI. For example, the provision unit can provide information using an AI model that takes the results managed by the management unit as input and outputs the information to be provided to the user.

[0030] The data collection unit collects user data. This data includes, but is not limited to, health data, behavioral data, and emotional data. For example, the unit collects user health data using sensors. Specifically, it uses sensors such as wearable devices and smartwatches to collect health data such as heart rate, blood pressure, body temperature, activity level, and sleep patterns in real time. This allows for continuous monitoring of the user's health status. The data collection unit can also collect user behavioral data through surveys. Surveys are an effective means of collecting information about users' daily activities and lifestyles, such as diet, exercise habits, stress levels, and sleep quality. Furthermore, the data collection unit can use facial recognition technology to collect user emotional data. For example, it can capture the user's facial expressions using a camera and estimate their emotional state using a facial recognition algorithm. This allows for real-time tracking of changes in the user's emotions. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and management departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, it can analyze user health data using statistical analysis to understand trends in health status. For example, it can analyze heart rate and blood pressure data to detect abnormal patterns and trends. It can also analyze user behavior data using machine learning algorithms. For example, it can learn a user's exercise habits and eating patterns to predict future behavior. Furthermore, the analysis unit can use natural language processing techniques to analyze emotional data. For example, it can analyze a user's facial expression and voice data to detect changes in emotion. This allows for real-time understanding of the user's emotional state and the provision of appropriate support. The analysis unit can also utilize historical data and statistical information to perform long-term health risk assessments and trend analyses. For example, it can predict fluctuations in specific health risks based on past health data and formulate future countermeasures. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Management Department manages hormone balance based on data analyzed by the Analysis Department. Hormone balance management is carried out using, for example, hormone level measurement and management methods, but is not limited to these examples. Specifically, the Management Department regularly measures the user's hormone levels and takes appropriate measures if abnormalities are detected. For example, hormone levels are measured using blood tests or saliva tests, and the results are analyzed. The Management Department can also manage hormone balance through lifestyle improvements. For example, reviewing diet and exercise is an effective way to regulate hormone balance. Specifically, a balanced diet and moderate exercise are recommended to manage stress and improve sleep quality. Furthermore, the Management Department can adjust hormone balance using hormone therapy. Hormone therapy is a treatment method performed under the guidance of a physician and is used to adjust hormone balance. For example, hormone replacement therapy or drug therapy is used to maintain hormone levels within the normal range. By combining these methods, the Management Department can comprehensively manage the user's hormone balance and maintain and improve their health. In addition, the Management Department can collect user feedback and continuously improve the accuracy and effectiveness of the management methods. This allows the management department to implement effective hormone balance management to maintain users' health in an optimal state.

[0033] The service provider delivers results managed by the management provider to the user. This delivery is, for example, through app notifications or email, but is not limited to these examples. Specifically, the service provider informs the user of changes in their health status through app notifications. For example, if abnormalities in heart rate or blood pressure are detected, it provides real-time notifications to encourage the user to take appropriate action. The service provider can also provide health management advice to the user through email. For example, it can create and send regular health reports to inform the user of changes in their health status and areas for improvement. Furthermore, the service provider can provide health information to the user through a web portal. The web portal functions as a platform where users can access and check their health information, viewing past data and analysis results. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can provide information using an AI model that takes results managed by the management provider as input and outputs information to be provided to the user. Specifically, the AI ​​model analyzes the user's health data and behavioral data to suggest individual health advice and preventative measures. This allows the service provider to provide users with timely and accurate information and support their health management. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. As a result, the service provider can provide users with optimal health support and improve the reliability and effectiveness of the entire system.

[0034] The service provider can provide relaxation tools. For example, the service provider can provide a meditation guide. The service provider can also provide relaxation music. The service provider can also provide a relaxation app. For example, the service provider can encourage relaxation in the user through a meditation guide. Relaxation music is used to provide an environment in which the user can relax. A relaxation app is provided as a tool that allows the user to easily relax at home. In this way, providing relaxation tools can promote relaxation in the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide relaxation tools using an AI model that takes the user's relaxation state as input and outputs an appropriate relaxation tool.

[0035] The service provider can provide exercise plans. For example, the service provider can provide exercise videos. The service provider can also provide training schedules. The service provider can also provide advice from a personal trainer. For example, the service provider can instruct users on how to exercise through exercise videos. Training schedules are provided as guidelines for users to exercise systematically. Advice from a personal trainer is provided as support for users to execute exercise plans tailored to their individual needs. In this way, providing exercise plans can support users in maintaining their health. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide exercise plans using an AI model that takes the user's exercise history as input and outputs an optimal exercise plan.

[0036] The service provider can provide meal plans. The service provider can provide, for example, recipes. The service provider can also provide a nutritional balance guide. The service provider can also provide a food diary. For example, the service provider can instruct users on how to eat healthily through recipes. The nutritional balance guide is provided as a guideline for users to eat a balanced diet. The food diary is provided as a tool for users to record their daily meals and use it for health management. In this way, providing meal plans can support users' nutritional management. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can provide meal plans using an AI model that takes the user's meal history as input and outputs an optimal meal plan.

[0037] The service provider can offer specialized medical consultations. For example, the service provider can offer online medical consultations. The service provider can also offer medical chatbots. The service provider can also offer video calls with medical professionals. For example, the service provider can provide medical consultations to users through online medical consultations. Medical chatbots are provided as a means for users to easily obtain medical information. Video calls with medical professionals are provided as a means for users to consult directly with medical professionals. By providing specialized medical consultations, the service provider can provide appropriate advice regarding the user's health problems. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide medical consultations using an AI model that takes the user's health status as input and outputs appropriate medical consultations.

[0038] The service provider can provide community forums. For example, the service provider can provide online bulletin boards. The service provider can also provide chat rooms. The service provider can also provide webinars. For example, the service provider can provide users with a platform for information exchange through online bulletin boards. Chat rooms are provided as a place where users can interact in real time. Webinars are provided as a place where users can deepen their knowledge through lectures and discussions by experts. In this way, providing community forums can promote interaction among users. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide community forums using an AI model that takes user interests and preferences as input and outputs appropriate community forums.

[0039] The service provider can provide audio guides that promote relaxation. For example, the service provider can provide meditation guides. The service provider can also provide relaxation audio. The service provider can also provide sleep induction audio. For example, the service provider can encourage relaxation in the user through meditation guides. Relaxation audio is used to provide an environment in which the user can relax. Sleep induction audio is provided as support for the user to fall asleep comfortably. Thus, by providing audio guides that promote relaxation, it is possible to promote relaxation in the user. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide audio guides using an AI model that takes the user's relaxation state as input and outputs an appropriate audio guide.

[0040] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can collect data at specific time periods based on the user's past health data. The data collection unit can also select specific data collection methods (e.g., wearable devices, questionnaires, etc.) based on the user's past health data. Furthermore, the data collection unit can analyze the user's past health data and focus on collecting data for specific health indicators. For example, the data collection unit can analyze the user's past health data and collect data at specific time periods. It can collect the user's health data using wearable devices. It can collect the user's behavioral data through questionnaire surveys. By analyzing the user's past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's past health data as input and selects the optimal data collection method to perform data collection.

[0041] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can collect only relevant data according to the user's current lifestyle. The data collection unit can also prioritize the collection of specific data based on the user's areas of interest. Furthermore, the data collection unit can filter out unnecessary data based on the user's lifestyle and areas of interest. For example, the data collection unit can collect only relevant data according to the user's lifestyle, prioritize the collection of specific data based on the user's areas of interest, and filter out unnecessary data. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's lifestyle and areas of interest as input and performs data collection filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of health data related to that region. The data collection unit can also collect data related to specific environmental factors based on the user's geographical location information. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to health risks at the travel destination. For example, the data collection unit will prioritize the collection of health data related to the user's geographical location information. It will also collect data related to specific environmental factors. It will also prioritize the collection of data related to health risks at the travel destination. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's geographical location information as input and prioritizes the collection of highly relevant data to perform data collection.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can estimate a user's current emotional state from their social media posts and collect relevant data. The data collection unit can also collect data related to areas of interest based on the user's social media activity. Furthermore, the data collection unit can analyze a user's social media activity and collect data related to specific health risks. For example, the data collection unit can estimate a user's emotional state from their social media posts and collect relevant data. It can collect data related to areas of interest. It can collect data related to specific health risks. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data collection using an AI model that takes a user's social media activity as input and collects relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance, a simplified analysis on data with low importance, and determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data importance as input and adjusts the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health data. It can also apply emotion analysis algorithms to emotion data. It can also apply lifestyle analysis algorithms to lifestyle data. For example, the analysis unit can apply health-related analysis algorithms to health data. It can apply emotion analysis algorithms to emotion data. It can apply lifestyle analysis algorithms to lifestyle data. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data categories as input and applies an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. For example, the analysis unit may prioritize the analysis of recently submitted data, postpone the analysis of older data, and adjust the analysis schedule based on the submission date. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes the data submission date as input and determines the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data, postpone the analysis of less relevant data, and adjust the analysis schedule based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes the relevance of the data as input and adjusts the order of analysis.

[0048] The management department can analyze the user's past health data during management to select the optimal management method. For example, the management department can select the optimal hormone balance management method from the user's past health data. The management department can also propose a specific hormone balance management method based on the user's past health data. Furthermore, the management department can analyze the user's past health data and select an effective hormone balance management method. For example, the management department can analyze the user's past health data and select the optimal hormone balance management method. It can propose a specific hormone balance management method. It can select an effective hormone balance management method. In this way, the optimal management method can be selected by analyzing the user's past health data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes the user's past health data as input and selects the optimal management method.

[0049] The management unit can customize the management means based on the user's current lifestyle during management. For example, the management unit can customize the hormone balance management means according to the user's current lifestyle. The management unit can also propose a specific hormone balance management method based on the user's lifestyle. Furthermore, the management unit can adjust the management means according to the user's lifestyle. For example, the management unit can customize the hormone balance management means according to the user's lifestyle, propose a specific hormone balance management method, and adjust the management means. This allows for more appropriate management by customizing the management means based on the user's lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can manage hormone balance using an AI model that takes the user's lifestyle as input and customizes the management means.

[0050] The management department can select the optimal management method when managing users, taking into account the user's geographical location. For example, if the user is in a specific region, the management department will select a hormone balance management method relevant to that region. The management department can also propose management methods related to specific environmental factors based on the user's geographical location. Furthermore, if the user is traveling, the management department can select a management method relevant to the health risks of the travel destination. For example, the management department can select a hormone balance management method relevant to the region based on the user's geographical location. It can propose management methods related to specific environmental factors. It can select management methods related to health risks of the travel destination. In this way, the optimal management method can be selected by taking the user's geographical location into consideration. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes the user's geographical location as input and selects the optimal management method.

[0051] The management department can analyze users' social media activity and propose management measures during management. For example, the management department can estimate the user's current emotional state from their social media posts and propose relevant management measures. The management department can also propose management measures related to the user's areas of interest based on their social media activity. Furthermore, the management department can analyze users' social media activity and propose management measures related to specific health risks. For example, the management department can estimate the user's emotional state from their social media posts and propose relevant management measures. It can propose management measures related to areas of interest. It can propose management measures related to specific health risks. In this way, by analyzing users' social media activity, relevant management measures can be proposed. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes users' social media activity as input and proposes relevant management measures.

[0052] The service provider can analyze the user's past usage history to select the most suitable service at the time of service provision. For example, the service provider can prioritize providing relaxation tools that the user has frequently used based on their past usage history. The service provider can also suggest a specific exercise plan based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and provide the most effective meal plan. For example, the service provider can analyze the user's past usage history and prioritize providing relaxation tools that the user has frequently used. It can suggest a specific exercise plan. It can provide the most effective meal plan. In this way, the service provider can select the most suitable service by analyzing the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide a service using an AI model that takes the user's past usage history as input and selects the most suitable service.

[0053] The service provider can customize the service content based on the user's current lifestyle at the time of delivery. For example, the service provider can customize relaxation tools according to the user's current lifestyle. The service provider can also suggest a specific exercise plan based on the user's lifestyle. The service provider can also adjust meal plans according to the user's lifestyle. For example, the service provider can customize relaxation tools according to the user's lifestyle, suggest a specific exercise plan, and adjust meal plans. By customizing the service content based on the user's lifestyle, a more appropriate service can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the service using an AI model that takes the user's lifestyle as input and customizes the service content.

[0054] The service provider can provide the most suitable service by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the service provider can provide relaxation tools related to that region. The service provider can also provide exercise plans related to specific environmental factors based on the user's geographical location. Furthermore, if the user is traveling, the service provider can provide services related to health risks at the travel destination. For example, the service provider can provide relaxation tools related to the region based on the user's geographical location. It can provide exercise plans related to specific environmental factors. It can provide services related to health risks at the travel destination. In this way, the service provider can provide the most suitable service by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide services using an AI model that takes the user's geographical location as input and provides the most suitable service.

[0055] The service provider can analyze the user's social media activity and propose service content at the time of service provision. For example, the service provider can estimate the user's current emotional state from their social media posts and provide relevant services. The service provider can also provide services related to the user's areas of interest based on their social media activity. Furthermore, the service provider can analyze the user's social media activity and provide services related to specific health risks. For example, the service provider can estimate the user's emotional state from their social media posts and provide relevant services. It can provide services related to areas of interest. It can provide services related to specific health risks. In this way, by analyzing the user's social media activity, it can propose relevant service content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide services using an AI model that takes the user's social media activity as input and proposes service content.

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

[0057] The health support system can further collect and analyze user sleep data. The collection unit monitors the user's sleep patterns, for example, using a wearable device. The collection unit can also collect data related to the user's sleep environment (e.g., room temperature, humidity, noise level, etc.). The analysis unit analyzes the collected sleep data and evaluates the user's sleep quality. For example, the analysis unit analyzes the user's sleep patterns and evaluates sleep depth and frequency of interruptions. Furthermore, the analysis unit can also analyze the user's sleep environment data and identify factors that affect sleep quality. The management unit makes suggestions to improve the user's sleep quality based on the analysis results. For example, the management unit provides the user with advice on creating a suitable sleep environment. The management unit can also suggest lifestyle improvements to the user to improve sleep quality. The delivery unit provides the user with the content suggested by the management unit. For example, the delivery unit informs the user of how to improve their sleep environment through app notifications. The delivery unit can also provide detailed sleep improvement advice to the user via email. In this way, the health support system can provide support to improve the user's sleep quality.

[0058] The health support system can further monitor the user's stress level in real time and suggest appropriate measures. The data collection unit, for example, monitors the user's heart rate and skin electrical activity using a wearable device to assess the stress level. The data collection unit can also collect stress level data from the user's self-reported data. The analysis unit analyzes the collected stress data to assess the user's stress level. For example, the analysis unit analyzes fluctuations in the user's heart rate and skin electrical activity to identify peak stress times. Furthermore, the analysis unit can also analyze the user's self-reported data to identify the causes and patterns of stress. The management unit makes suggestions to reduce the user's stress based on the analysis results. For example, the management unit suggests relaxation techniques and stress management methods to the user. The management unit can also suggest lifestyle improvements to reduce stress. The delivery unit provides the content suggested by the management unit to the user. For example, the delivery unit informs the user about stress management methods through app notifications. The delivery unit can also provide detailed stress reduction advice to the user via email. This allows the health support system to monitor the user's stress level in real time and suggest appropriate countermeasures.

[0059] The health support system can further collect and analyze user dietary data. The collection unit, for example, provides an app for users to record their meals. The collection unit can also collect self-reported data about users' diets. The analysis unit analyzes the collected dietary data and evaluates the user's nutritional balance. For example, the analysis unit analyzes the user's meals and evaluates nutrient intake. Furthermore, the analysis unit can analyze the user's eating patterns and identify imbalances in their nutrition. The management unit makes suggestions to improve the user's nutritional balance based on the analysis results. For example, the management unit proposes a balanced meal plan to the user. The management unit can also suggest meals to increase the intake of specific nutrients. The delivery unit provides the content suggested by the management unit to the user. For example, the delivery unit notifies the user of the meal plan through app notifications. The delivery unit can also provide detailed meal suggestions to the user via email. In this way, the health support system can provide support to improve the user's nutritional balance.

[0060] The health support system can further collect and analyze user exercise data. The collection unit monitors the user's exercise volume and patterns, for example, using wearable devices. The collection unit can also collect exercise data based on the user's self-reported data. The analysis unit analyzes the collected exercise data and evaluates the user's exercise habits. For example, the analysis unit analyzes the user's exercise volume and assesses tendencies toward insufficient or excessive exercise. Furthermore, the analysis unit can also analyze the user's exercise patterns and evaluate the quality of exercise. The management unit makes suggestions to improve the user's exercise habits based on the analysis results. For example, the management unit proposes an appropriate exercise plan to the user. The management unit can also provide advice to the user to improve the quality of their exercise. The delivery unit provides the exercise plan proposed by the management unit to the user. For example, the delivery unit notifies the user of the exercise plan through app notifications. The delivery unit can also provide detailed exercise suggestions to the user via email. In this way, the health support system can provide support to improve the user's exercise habits.

[0061] The health support system can further consider the user's geographical location to provide services that address region-specific health risks. The data collection unit, for example, uses GPS technology to identify the user's current location. The data collection unit can also collect location data based on the user's self-reported information. The analysis unit analyzes the collected location data to assess region-specific health risks the user may face. For example, the analysis unit identifies health risks based on the user's current location, taking into account regional climate and environmental factors. Furthermore, the analysis unit can analyze the user's location data to assess health risks at travel destinations. The management unit proposes health risk countermeasures tailored to the user's geographical location based on the analysis results. For example, the management unit proposes preventative measures to address region-specific health risks. The management unit can also provide advice to users regarding health risks at travel destinations. The delivery unit provides the health risk countermeasures proposed by the management unit to the user. For example, the delivery unit informs the user of health risk countermeasures through app notifications. The delivery unit can also provide detailed health risk countermeasure information to the user via email. This allows the health support system to take the user's geographical location into consideration and provide services that address region-specific health risks.

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

[0063] Step 1: The data collection unit collects user data. User data includes health data, behavioral data, and emotional data. The data collection unit collects user health data using sensors, behavioral data through questionnaires, and emotional data using facial recognition technology. For example, it collects health data by monitoring heart rate and activity levels using wearable devices. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and natural language processing techniques. For example, health data is analyzed using statistical analysis, behavioral data is analyzed using machine learning algorithms, and emotional data is analyzed using natural language processing techniques. Step 3: The management department manages hormone balance based on the data analyzed by the analysis department. Hormone balance management is carried out through hormone level measurement, suggestion of management methods, lifestyle improvements, and hormone therapy. For example, regular hormone level measurements are performed, and appropriate measures are taken if abnormalities are detected. Step 4: The service provider delivers the results managed by the management department to the user. This delivery is done through app notifications, email, and a web portal. For example, real-time changes in health status are notified using app notifications, detailed health management advice is provided via email, and health information is provided through a web portal.

[0064] (Example of form 2) The health support system according to an embodiment of the present invention is a tool for supporting the mental and physical health of women in menopause. This health support system uses AI technology to help women achieve a mental and physical state that is not at the mercy of hormonal imbalances, and to live each day comfortably. The target audience includes women who are experiencing or about to experience menopause, those who need information related to menopause, and those who are suffering from symptoms. One of the challenges faced by these target audiences is that accurate information about menopause is not readily available, making it difficult to find appropriate ways to cope with symptoms and changes. To address this challenge, the system provides specialized medical consultations, learning content, exercise and meal plans for a healthy lifestyle, community forums for interaction among those experiencing menopause, and audio guides to promote relaxation, among other resources for menopausal women to obtain information. In terms of market size, there are billions of women worldwide who experience menopause, and many of them are seeking information and support during this period. There is a need for a platform that allows them to easily obtain the information they need, and generative AI technology, which provides solutions to the unique experiences and problems of women in menopause, shows the potential to meet this demand. Through this platform, we aim to provide support to empower menopausal women to take ownership of their own health, helping them manage menopausal symptoms and emotional fluctuations, and leading healthier and more fulfilling lives. In this way, the health support system can support the mental and physical health of the menopausal generation.

[0065] The health support system according to the embodiment comprises a data collection unit, an analysis unit, a management unit, and a data provision unit. The data collection unit collects user data. User data includes, but is not limited to, health data, behavioral data, and emotional data. The data collection unit collects user health data, for example, using sensors. The data collection unit can also collect user behavioral data through questionnaire surveys. Furthermore, the data collection unit may use facial recognition technology to collect user emotional data. For example, the data collection unit monitors the user's heart rate and activity level using a wearable device and collects health data. Questionnaire surveys are an effective means of collecting information about the user's daily activities and lifestyle. Facial recognition technology is used to analyze the user's facial expressions and estimate their emotional state. The analysis unit analyzes the data collected by the data collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to these examples. For example, the analysis unit analyzes user health data using statistical analysis. The analysis unit can also analyze user behavioral data using machine learning algorithms. Furthermore, the analysis unit can also use natural language processing technology to analyze emotional data. For example, the analysis unit statistically analyzes the user's health data to understand trends in their health status. Machine learning algorithms are used to learn the user's behavior patterns and predict future behavior. Natural language processing technology is used to analyze the user's emotional data and detect changes in emotions. The management unit manages hormone balance based on the data analyzed by the analysis unit. Hormone balance management is carried out, for example, by measuring and managing hormone levels, but is not limited to such examples. For example, the management unit measures hormone levels and proposes appropriate management measures. The management unit can also manage hormone balance through lifestyle improvements. The management unit can also adjust hormone balance using hormone therapy. For example, the management unit performs regular hormone level measurements and takes appropriate measures if abnormalities are detected. Lifestyle improvements are an effective means of regulating hormone balance through a review of diet and exercise.Hormone therapy is a treatment method performed under the guidance of a physician and is used to adjust hormone balance. The provision unit provides the user with the results managed by the management unit. The provision is carried out, for example, through app notifications or email, but is not limited to such examples. For example, the provision unit informs the user of changes in their health status through app notifications. The provision unit can also provide the user with health management advice through email. The provision unit can also provide the user with health information through a web portal. For example, the provision unit notifies the user of changes in their health status in real time using app notifications. Email is an effective means of providing the user with detailed health management advice. The web portal functions as a platform that the user can access and check health information. In this way, the health support system according to the embodiment can support the mental and physical health of the menopausal generation. Some or all of the processing described above in the provision unit may be carried out using, for example, AI, or not using AI. For example, the provision unit can provide information using an AI model that takes the results managed by the management unit as input and outputs the information to be provided to the user.

[0066] The data collection unit collects user data. This data includes, but is not limited to, health data, behavioral data, and emotional data. For example, the unit collects user health data using sensors. Specifically, it uses sensors such as wearable devices and smartwatches to collect health data such as heart rate, blood pressure, body temperature, activity level, and sleep patterns in real time. This allows for continuous monitoring of the user's health status. The data collection unit can also collect user behavioral data through surveys. Surveys are an effective means of collecting information about users' daily activities and lifestyles, such as diet, exercise habits, stress levels, and sleep quality. Furthermore, the data collection unit can use facial recognition technology to collect user emotional data. For example, it can capture the user's facial expressions using a camera and estimate their emotional state using a facial recognition algorithm. This allows for real-time tracking of changes in the user's emotions. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and management departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the collection unit. Analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, it can analyze user health data using statistical analysis to understand trends in health status. For example, it can analyze heart rate and blood pressure data to detect abnormal patterns and trends. It can also analyze user behavior data using machine learning algorithms. For example, it can learn a user's exercise habits and eating patterns to predict future behavior. Furthermore, the analysis unit can use natural language processing techniques to analyze emotional data. For example, it can analyze a user's facial expression and voice data to detect changes in emotion. This allows for real-time understanding of the user's emotional state and the provision of appropriate support. The analysis unit can also utilize historical data and statistical information to perform long-term health risk assessments and trend analyses. For example, it can predict fluctuations in specific health risks based on past health data and formulate future countermeasures. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0068] The Management Department manages hormone balance based on data analyzed by the Analysis Department. Hormone balance management is carried out using, for example, hormone level measurement and management methods, but is not limited to these examples. Specifically, the Management Department regularly measures the user's hormone levels and takes appropriate measures if abnormalities are detected. For example, hormone levels are measured using blood tests or saliva tests, and the results are analyzed. The Management Department can also manage hormone balance through lifestyle improvements. For example, reviewing diet and exercise is an effective way to regulate hormone balance. Specifically, a balanced diet and moderate exercise are recommended to manage stress and improve sleep quality. Furthermore, the Management Department can adjust hormone balance using hormone therapy. Hormone therapy is a treatment method performed under the guidance of a physician and is used to adjust hormone balance. For example, hormone replacement therapy or drug therapy is used to maintain hormone levels within the normal range. By combining these methods, the Management Department can comprehensively manage the user's hormone balance and maintain and improve their health. In addition, the Management Department can collect user feedback and continuously improve the accuracy and effectiveness of the management methods. This allows the management department to implement effective hormone balance management to maintain users' health in an optimal state.

[0069] The service provider delivers results managed by the management provider to the user. This delivery is, for example, through app notifications or email, but is not limited to these examples. Specifically, the service provider informs the user of changes in their health status through app notifications. For example, if abnormalities in heart rate or blood pressure are detected, it provides real-time notifications to encourage the user to take appropriate action. The service provider can also provide health management advice to the user through email. For example, it can create and send regular health reports to inform the user of changes in their health status and areas for improvement. Furthermore, the service provider can provide health information to the user through a web portal. The web portal functions as a platform where users can access and check their health information, viewing past data and analysis results. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can provide information using an AI model that takes results managed by the management provider as input and outputs information to be provided to the user. Specifically, the AI ​​model analyzes the user's health data and behavioral data to suggest individual health advice and preventative measures. This allows the service provider to provide users with timely and accurate information and support their health management. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the services provided. As a result, the service provider can provide users with optimal health support and improve the reliability and effectiveness of the entire system.

[0070] The service provider can provide relaxation tools. For example, the service provider can provide a meditation guide. The service provider can also provide relaxation music. The service provider can also provide a relaxation app. For example, the service provider can encourage relaxation in the user through a meditation guide. Relaxation music is used to provide an environment in which the user can relax. A relaxation app is provided as a tool that allows the user to easily relax at home. In this way, providing relaxation tools can promote relaxation in the user. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide relaxation tools using an AI model that takes the user's relaxation state as input and outputs an appropriate relaxation tool.

[0071] The service provider can provide exercise plans. For example, the service provider can provide exercise videos. The service provider can also provide training schedules. The service provider can also provide advice from a personal trainer. For example, the service provider can instruct users on how to exercise through exercise videos. Training schedules are provided as guidelines for users to exercise systematically. Advice from a personal trainer is provided as support for users to execute exercise plans tailored to their individual needs. In this way, providing exercise plans can support users in maintaining their health. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide exercise plans using an AI model that takes the user's exercise history as input and outputs an optimal exercise plan.

[0072] The service provider can provide meal plans. The service provider can provide, for example, recipes. The service provider can also provide a nutritional balance guide. The service provider can also provide a food diary. For example, the service provider can instruct users on how to eat healthily through recipes. The nutritional balance guide is provided as a guideline for users to eat a balanced diet. The food diary is provided as a tool for users to record their daily meals and use it for health management. In this way, providing meal plans can support users' nutritional management. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can provide meal plans using an AI model that takes the user's meal history as input and outputs an optimal meal plan.

[0073] The service provider can offer specialized medical consultations. For example, the service provider can offer online medical consultations. The service provider can also offer medical chatbots. The service provider can also offer video calls with medical professionals. For example, the service provider can provide medical consultations to users through online medical consultations. Medical chatbots are provided as a means for users to easily obtain medical information. Video calls with medical professionals are provided as a means for users to consult directly with medical professionals. By providing specialized medical consultations, the service provider can provide appropriate advice regarding the user's health problems. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide medical consultations using an AI model that takes the user's health status as input and outputs appropriate medical consultations.

[0074] The service provider can provide community forums. For example, the service provider can provide online bulletin boards. The service provider can also provide chat rooms. The service provider can also provide webinars. For example, the service provider can provide users with a platform for information exchange through online bulletin boards. Chat rooms are provided as a place where users can interact in real time. Webinars are provided as a place where users can deepen their knowledge through lectures and discussions by experts. In this way, providing community forums can promote interaction among users. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide community forums using an AI model that takes user interests and preferences as input and outputs appropriate community forums.

[0075] The service provider can provide audio guides that promote relaxation. For example, the service provider can provide meditation guides. The service provider can also provide relaxation audio. The service provider can also provide sleep induction audio. For example, the service provider can encourage relaxation in the user through meditation guides. Relaxation audio is used to provide an environment in which the user can relax. Sleep induction audio is provided as support for the user to fall asleep comfortably. Thus, by providing audio guides that promote relaxation, it is possible to promote relaxation in the user. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide audio guides using an AI model that takes the user's relaxation state as input and outputs an appropriate audio guide.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect data when the user is relaxed. The data collection unit can also increase the frequency to collect more detailed data when the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can perform simplified data collection and later supplement it with more detailed data. For example, the data collection unit can estimate the user's emotions using facial recognition technology and reduce the frequency of data collection if the user is stressed. If the user is relaxed, it can increase the frequency to collect more detailed data. If the user is in a hurry, it can perform simplified data collection and later supplement it with more detailed data. This allows for the collection of more appropriate data by adjusting the timing of 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. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's emotional state as input and adjusts the timing of data collection to perform data collection.

[0077] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can collect data at specific time periods based on the user's past health data. The data collection unit can also select specific data collection methods (e.g., wearable devices, questionnaires, etc.) based on the user's past health data. Furthermore, the data collection unit can analyze the user's past health data and focus on collecting data for specific health indicators. For example, the data collection unit can analyze the user's past health data and collect data at specific time periods. It can collect the user's health data using wearable devices. It can collect the user's behavioral data through questionnaire surveys. By analyzing the user's past health data, the optimal data collection method can be selected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's past health data as input and selects the optimal data collection method to perform data collection.

[0078] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can collect only relevant data according to the user's current lifestyle. The data collection unit can also prioritize the collection of specific data based on the user's areas of interest. Furthermore, the data collection unit can filter out unnecessary data based on the user's lifestyle and areas of interest. For example, the data collection unit can collect only relevant data according to the user's lifestyle, prioritize the collection of specific data based on the user's areas of interest, and filter out unnecessary data. This allows for the collection of highly relevant data by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's lifestyle and areas of interest as input and performs data collection filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. It can also prioritize collecting relaxation-related data if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize collecting time-related data. For example, the data collection unit can estimate the user's emotions using facial recognition technology and prioritize collecting stress-related data if the user is stressed. If the user is relaxed, it will prioritize collecting relaxation-related data. If the user is in a hurry, it will prioritize collecting time-related data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using an AI model that takes the user's emotional state as input and determines the priority of the data.

[0080] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of health data related to that region. The data collection unit can also collect data related to specific environmental factors based on the user's geographical location information. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of data related to health risks at the travel destination. For example, the data collection unit will prioritize the collection of health data related to the user's geographical location information. It will also collect data related to specific environmental factors. It will also prioritize the collection of data related to health risks at the travel destination. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's geographical location information as input and prioritizes the collection of highly relevant data to perform data collection.

[0081] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can estimate a user's current emotional state from their social media posts and collect relevant data. The data collection unit can also collect data related to areas of interest based on the user's social media activity. Furthermore, the data collection unit can analyze a user's social media activity and collect data related to specific health risks. For example, the data collection unit can estimate a user's emotional state from their social media posts and collect relevant data. It can collect data related to areas of interest. It can collect data related to specific health risks. In this way, relevant data can be collected by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data collection using an AI model that takes a user's social media activity as input and collects relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit will focus on analyzing stress-related data. If the user is relaxed, the analysis unit can also analyze relaxation-related data in detail. Furthermore, if the user is in a hurry, the analysis unit can use a simplified analysis method to provide results quickly. For example, the analysis unit estimates emotions using facial recognition technology and, if the user is stressed, focuses on analyzing stress-related data. If the user is relaxed, it analyzes relaxation-related data in detail. If the user is in a hurry, it uses a simplified analysis method to provide results quickly. This allows for more appropriate analysis results by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take the user's emotional state as input and perform data analysis using an AI model that adjusts the data analysis method.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance, a simplified analysis on data with low importance, and determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data importance as input and adjusts the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health data. It can also apply emotion analysis algorithms to emotion data. It can also apply lifestyle analysis algorithms to lifestyle data. For example, the analysis unit can apply health-related analysis algorithms to health data. It can apply emotion analysis algorithms to emotion data. It can apply lifestyle analysis algorithms to lifestyle data. By applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes data categories as input and applies an appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a display method that focuses on the essentials. For example, the analysis unit estimates emotions using facial recognition technology and provides a simple and highly visible display method if the user is stressed. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that focuses on the essentials. This allows for a user-friendly display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can take the user's emotional state as input and display the analysis results using an AI model that adjusts how the analysis results are displayed.

[0086] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the analysis schedule based on the submission date. For example, the analysis unit may prioritize the analysis of recently submitted data, postpone the analysis of older data, and adjust the analysis schedule based on the submission date. This enables efficient data analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes the data submission date as input and determines the priority of analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the analysis schedule based on the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data, postpone the analysis of less relevant data, and adjust the analysis schedule based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform data analysis using an AI model that takes the relevance of the data as input and adjusts the order of analysis.

[0088] The management unit can estimate the user's emotions and adjust the hormone balance management method based on the estimated emotions. For example, if the user is feeling stressed, the management unit can suggest a hormone balance management method to reduce stress. The management unit can also suggest a hormone balance management method to promote relaxation if the user is relaxed. Furthermore, if the user is in a hurry, the management unit can suggest a hormone balance management method that produces a quick effect. For example, the management unit can estimate the user's emotions using facial recognition technology and, if the user is stressed, suggest a hormone balance management method to reduce stress. If the user is relaxed, suggest a hormone balance management method to promote relaxation. If the user is in a hurry, suggest a hormone balance management method that produces a quick effect. This allows for more appropriate management by adjusting the hormone balance management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes the user's emotional state as input and adjusts the method of managing hormone balance.

[0089] The management department can analyze the user's past health data during management to select the optimal management method. For example, the management department can select the optimal hormone balance management method from the user's past health data. The management department can also propose a specific hormone balance management method based on the user's past health data. Furthermore, the management department can analyze the user's past health data and select an effective hormone balance management method. For example, the management department can analyze the user's past health data and select the optimal hormone balance management method. It can propose a specific hormone balance management method. It can select an effective hormone balance management method. In this way, the optimal management method can be selected by analyzing the user's past health data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes the user's past health data as input and selects the optimal management method.

[0090] The management unit can customize the management means based on the user's current lifestyle during management. For example, the management unit can customize the hormone balance management means according to the user's current lifestyle. The management unit can also propose a specific hormone balance management method based on the user's lifestyle. Furthermore, the management unit can adjust the management means according to the user's lifestyle. For example, the management unit can customize the hormone balance management means according to the user's lifestyle, propose a specific hormone balance management method, and adjust the management means. This allows for more appropriate management by customizing the management means based on the user's lifestyle. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can manage hormone balance using an AI model that takes the user's lifestyle as input and customizes the management means.

[0091] The management department can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is stressed, the management department will prioritize stress-reducing management. If the user is relaxed, the management department may also prioritize management that promotes relaxation. Furthermore, if the user is in a hurry, the management department may prioritize management that produces quick results. For example, the management department can estimate the user's emotions using facial recognition technology and prioritize stress-reducing management if the user is stressed. If the user is relaxed, it will prioritize management that promotes relaxation. If the user is in a hurry, it will prioritize management that produces quick results. This allows important management to be prioritized by determining management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes the user's emotional state as input and determines management priorities.

[0092] The management department can select the optimal management method when managing users, taking into account the user's geographical location. For example, if the user is in a specific region, the management department will select a hormone balance management method relevant to that region. The management department can also propose management methods related to specific environmental factors based on the user's geographical location. Furthermore, if the user is traveling, the management department can select a management method relevant to the health risks of the travel destination. For example, the management department can select a hormone balance management method relevant to the region based on the user's geographical location. It can propose management methods related to specific environmental factors. It can select management methods related to health risks of the travel destination. In this way, the optimal management method can be selected by taking the user's geographical location into consideration. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes the user's geographical location as input and selects the optimal management method.

[0093] The management department can analyze users' social media activity and propose management measures during management. For example, the management department can estimate the user's current emotional state from their social media posts and propose relevant management measures. The management department can also propose management measures related to the user's areas of interest based on their social media activity. Furthermore, the management department can analyze users' social media activity and propose management measures related to specific health risks. For example, the management department can estimate the user's emotional state from their social media posts and propose relevant management measures. It can propose management measures related to areas of interest. It can propose management measures related to specific health risks. In this way, by analyzing users' social media activity, relevant management measures can be proposed. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can manage hormone balance using an AI model that takes users' social media activity as input and proposes relevant management measures.

[0094] The service provider can estimate the user's emotions and adjust the content of the services provided based on the estimated emotions. For example, if the user is feeling stressed, the service provider can provide relaxation tools to reduce stress. If the user is relaxed, the service provider can also provide audio guidance to promote relaxation. If the user is in a hurry, the service provider can also provide an exercise plan that produces quick results. For example, the service provider can estimate the user's emotions using facial recognition technology and provide relaxation tools to reduce stress if the user is feeling stressed. If the user is relaxed, it can provide audio guidance to promote relaxation. If the user is in a hurry, it can provide an exercise plan that produces quick results. This allows the service provider to provide more appropriate services by adjusting the content of the services according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use an AI model that takes the user's emotional state as input and adjusts the service content accordingly to provide the service.

[0095] The service provider can analyze the user's past usage history to select the most suitable service at the time of service provision. For example, the service provider can prioritize providing relaxation tools that the user has frequently used based on their past usage history. The service provider can also suggest a specific exercise plan based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and provide the most effective meal plan. For example, the service provider can analyze the user's past usage history and prioritize providing relaxation tools that the user has frequently used. It can suggest a specific exercise plan. It can provide the most effective meal plan. In this way, the service provider can select the most suitable service by analyzing the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide a service using an AI model that takes the user's past usage history as input and selects the most suitable service.

[0096] The service provider can customize the service content based on the user's current lifestyle at the time of delivery. For example, the service provider can customize relaxation tools according to the user's current lifestyle. The service provider can also suggest a specific exercise plan based on the user's lifestyle. The service provider can also adjust meal plans according to the user's lifestyle. For example, the service provider can customize relaxation tools according to the user's lifestyle, suggest a specific exercise plan, and adjust meal plans. By customizing the service content based on the user's lifestyle, a more appropriate service can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the service using an AI model that takes the user's lifestyle as input and customizes the service content.

[0097] The service provider can estimate the user's emotions and determine the priority of services to provide based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing services to reduce stress. If the user is relaxed, the service provider can also prioritize providing services that promote relaxation. Furthermore, if the user is in a hurry, the service provider can prioritize providing services that produce quick results. For example, the service provider can estimate the user's emotions using facial recognition technology and, if the user is stressed, prioritize providing services to reduce stress. If the user is relaxed, it will prioritize providing services that promote relaxation. If the user is in a hurry, it will prioritize providing services that produce quick results. This allows for the priority of important services by determining service priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use an AI model that takes the user's emotional state as input and determines the priority of services to provide those services.

[0098] The service provider can provide the most suitable service by considering the user's geographical location at the time of delivery. For example, if the user is in a specific region, the service provider can provide relaxation tools related to that region. The service provider can also provide exercise plans related to specific environmental factors based on the user's geographical location. Furthermore, if the user is traveling, the service provider can provide services related to health risks at the travel destination. For example, the service provider can provide relaxation tools related to the region based on the user's geographical location. It can provide exercise plans related to specific environmental factors. It can provide services related to health risks at the travel destination. In this way, the service provider can provide the most suitable service by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide services using an AI model that takes the user's geographical location as input and provides the most suitable service.

[0099] The service provider can analyze the user's social media activity and propose service content at the time of service provision. For example, the service provider can estimate the user's current emotional state from their social media posts and provide relevant services. The service provider can also provide services related to the user's areas of interest based on their social media activity. Furthermore, the service provider can analyze the user's social media activity and provide services related to specific health risks. For example, the service provider can estimate the user's emotional state from their social media posts and provide relevant services. It can provide services related to areas of interest. It can provide services related to specific health risks. In this way, by analyzing the user's social media activity, it can propose relevant service content. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide services using an AI model that takes the user's social media activity as input and proposes service content.

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

[0101] The health support system can further collect and analyze user sleep data. The collection unit monitors the user's sleep patterns, for example, using a wearable device. The collection unit can also collect data related to the user's sleep environment (e.g., room temperature, humidity, noise level, etc.). The analysis unit analyzes the collected sleep data and evaluates the user's sleep quality. For example, the analysis unit analyzes the user's sleep patterns and evaluates sleep depth and frequency of interruptions. Furthermore, the analysis unit can also analyze the user's sleep environment data and identify factors that affect sleep quality. The management unit makes suggestions to improve the user's sleep quality based on the analysis results. For example, the management unit provides the user with advice on creating a suitable sleep environment. The management unit can also suggest lifestyle improvements to the user to improve sleep quality. The delivery unit provides the user with the content suggested by the management unit. For example, the delivery unit informs the user of how to improve their sleep environment through app notifications. The delivery unit can also provide detailed sleep improvement advice to the user via email. In this way, the health support system can provide support to improve the user's sleep quality.

[0102] The health support system can further monitor the user's stress level in real time and suggest appropriate measures. The data collection unit, for example, monitors the user's heart rate and skin electrical activity using a wearable device to assess the stress level. The data collection unit can also collect stress level data from the user's self-reported data. The analysis unit analyzes the collected stress data to assess the user's stress level. For example, the analysis unit analyzes fluctuations in the user's heart rate and skin electrical activity to identify peak stress times. Furthermore, the analysis unit can also analyze the user's self-reported data to identify the causes and patterns of stress. The management unit makes suggestions to reduce the user's stress based on the analysis results. For example, the management unit suggests relaxation techniques and stress management methods to the user. The management unit can also suggest lifestyle improvements to reduce stress. The delivery unit provides the content suggested by the management unit to the user. For example, the delivery unit informs the user about stress management methods through app notifications. The delivery unit can also provide detailed stress reduction advice to the user via email. This allows the health support system to monitor the user's stress level in real time and suggest appropriate countermeasures.

[0103] The health support system can further estimate the user's emotions and adjust the exercise plan based on those emotions. The data collection unit estimates the user's emotions, for example, using facial recognition technology. The data collection unit can also collect emotion data from the user's self-reported data. The analysis unit analyzes the collected emotion data and evaluates the user's emotional state. For example, the analysis unit analyzes the user's facial data and detects changes in emotion. Furthermore, the analysis unit can also analyze the user's self-reported data and identify emotional patterns. The management unit proposes an exercise plan that suits the user's emotional state based on the analysis results. For example, if the user is feeling stressed, the management unit will propose an exercise plan with a relaxation effect. If the user is relaxed, the management unit can also propose an energetic exercise plan. The delivery unit provides the user with the exercise plan proposed by the management unit. For example, the delivery unit will notify the user of the exercise plan through app notifications. The delivery unit can also provide the user with a detailed exercise plan via email. In this way, the health support system can provide an exercise plan that suits the user's emotional state.

[0104] The health support system can further collect and analyze user dietary data. The collection unit, for example, provides an app for users to record their meals. The collection unit can also collect self-reported data about users' diets. The analysis unit analyzes the collected dietary data and evaluates the user's nutritional balance. For example, the analysis unit analyzes the user's meals and evaluates nutrient intake. Furthermore, the analysis unit can analyze the user's eating patterns and identify imbalances in their nutrition. The management unit makes suggestions to improve the user's nutritional balance based on the analysis results. For example, the management unit proposes a balanced meal plan to the user. The management unit can also suggest meals to increase the intake of specific nutrients. The delivery unit provides the content suggested by the management unit to the user. For example, the delivery unit notifies the user of the meal plan through app notifications. The delivery unit can also provide detailed meal suggestions to the user via email. In this way, the health support system can provide support to improve the user's nutritional balance.

[0105] The health support system can further estimate the user's emotions and provide relaxation tools based on those estimated emotions. The data collection unit estimates the user's emotions, for example, using facial recognition technology. The data collection unit can also collect emotion data from the user's self-reported data. The analysis unit analyzes the collected emotion data and evaluates the user's emotional state. For example, the analysis unit analyzes the user's facial data and detects changes in emotion. Furthermore, the analysis unit can also analyze the user's self-reported data and identify emotional patterns. The management unit proposes relaxation tools according to the user's emotional state based on the analysis results. For example, if the user is feeling stressed, the management unit may suggest relaxation music or meditation guides. If the user is relaxed, the management unit may also suggest audio guides to promote relaxation. The delivery unit provides the relaxation tools proposed by the management unit to the user. For example, the delivery unit notifies the user of relaxation tools through app notifications. The delivery unit can also provide the user with detailed relaxation suggestions via email. This allows the health support system to provide relaxation tools tailored to the user's emotional state.

[0106] The health support system can further collect and analyze user exercise data. The collection unit monitors the user's exercise volume and patterns, for example, using wearable devices. The collection unit can also collect exercise data based on the user's self-reported data. The analysis unit analyzes the collected exercise data and evaluates the user's exercise habits. For example, the analysis unit analyzes the user's exercise volume and assesses tendencies toward insufficient or excessive exercise. Furthermore, the analysis unit can also analyze the user's exercise patterns and evaluate the quality of exercise. The management unit makes suggestions to improve the user's exercise habits based on the analysis results. For example, the management unit proposes an appropriate exercise plan to the user. The management unit can also provide advice to the user to improve the quality of their exercise. The delivery unit provides the exercise plan proposed by the management unit to the user. For example, the delivery unit notifies the user of the exercise plan through app notifications. The delivery unit can also provide detailed exercise suggestions to the user via email. In this way, the health support system can provide support to improve the user's exercise habits.

[0107] The health support system can further estimate the user's emotions and adjust the meal plan based on those emotions. The data collection unit estimates the user's emotions, for example, using facial recognition technology. The data collection unit can also collect emotion data from the user's self-reported responses. The analysis unit analyzes the collected emotion data and evaluates the user's emotional state. For example, the analysis unit analyzes the user's facial expression data to detect changes in emotion. Furthermore, the analysis unit can also analyze the user's self-reported data to identify emotional patterns. The management unit proposes a meal plan tailored to the user's emotional state based on the analysis results. For example, if the user is feeling stressed, the management unit proposes a meal plan that helps reduce stress. If the user is relaxed, the management unit can also propose a meal plan that promotes relaxation. The delivery unit provides the meal plan proposed by the management unit to the user. For example, the delivery unit notifies the user of the meal plan through app notifications. The delivery unit can also provide the user with detailed meal suggestions via email. In this way, the health support system can provide a meal plan tailored to the user's emotional state.

[0108] The health support system can further estimate the user's emotions and adjust the content of the community forum based on the estimated emotions. The data collection unit estimates the user's emotions, for example, using facial recognition technology. The data collection unit can also collect emotion data from the user's self-reported responses. The analysis unit analyzes the collected emotion data and evaluates the user's emotional state. For example, the analysis unit analyzes the user's facial expression data and detects changes in emotion. Furthermore, the analysis unit can also analyze the user's self-reported data and identify emotional patterns. The management unit proposes community forum content tailored to the user's emotional state based on the analysis results. For example, if the user is feeling stressed, the management unit proposes a forum that provides information and support to help reduce stress. If the user is relaxed, the management unit can also propose a forum that provides information and support to promote relaxation. The delivery unit provides the community forum content proposed by the management unit to the user. For example, the delivery unit informs the user of the forum content through app notifications. The delivery unit can also provide detailed forum information to the user through email. In this way, the health support system can provide community forum content tailored to the user's emotional state.

[0109] The health support system can further estimate the user's emotions and determine the priority of services to provide based on those estimated emotions. The data collection unit estimates the user's emotions, for example, using facial recognition technology. The data collection unit can also collect emotion data from the user's self-reported responses. The analysis unit analyzes the collected emotion data and evaluates the user's emotional state. For example, the analysis unit analyzes the user's facial expression data to detect changes in emotion. Furthermore, the analysis unit can also analyze the user's self-reported data to identify emotional patterns. The management unit determines the priority of services according to the user's emotional state based on the analysis results. For example, if the user is feeling stressed, the management unit will prioritize providing services to reduce stress. The management unit can also prioritize providing services that promote relaxation if the user is relaxed. The service delivery unit provides services to the user based on the service priorities determined by the management unit. For example, the service delivery unit will inform the user of priority services through app notifications. The service delivery unit can also provide users with detailed service information through email. In this way, the health support system can determine the priority of services according to the user's emotional state and provide appropriate services.

[0110] The health support system can further consider the user's geographical location to provide services that address region-specific health risks. The data collection unit, for example, uses GPS technology to identify the user's current location. The data collection unit can also collect location data based on the user's self-reported information. The analysis unit analyzes the collected location data to assess region-specific health risks the user may face. For example, the analysis unit identifies health risks based on the user's current location, taking into account regional climate and environmental factors. Furthermore, the analysis unit can analyze the user's location data to assess health risks at travel destinations. The management unit proposes health risk countermeasures tailored to the user's geographical location based on the analysis results. For example, the management unit proposes preventative measures to address region-specific health risks. The management unit can also provide advice to users regarding health risks at travel destinations. The delivery unit provides the health risk countermeasures proposed by the management unit to the user. For example, the delivery unit informs the user of health risk countermeasures through app notifications. The delivery unit can also provide detailed health risk countermeasure information to the user via email. This allows the health support system to take the user's geographical location into consideration and provide services that address region-specific health risks.

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

[0112] Step 1: The data collection unit collects user data. User data includes health data, behavioral data, and emotional data. The data collection unit collects user health data using sensors, behavioral data through questionnaires, and emotional data using facial recognition technology. For example, it collects health data by monitoring heart rate and activity levels using wearable devices. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis, machine learning algorithms, and natural language processing techniques. For example, health data is analyzed using statistical analysis, behavioral data is analyzed using machine learning algorithms, and emotional data is analyzed using natural language processing techniques. Step 3: The management department manages hormone balance based on the data analyzed by the analysis department. Hormone balance management is carried out through hormone level measurement, suggestion of management methods, lifestyle improvements, and hormone therapy. For example, regular hormone level measurements are performed, and appropriate measures are taken if abnormalities are detected. Step 4: The service provider delivers the results managed by the management department to the user. This delivery is done through app notifications, email, and a web portal. For example, real-time changes in health status are notified using app notifications, detailed health management advice is provided via email, and health information is provided through a web portal.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user health data using sensors or wearable devices of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The management unit manages hormone balance by the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the results managed by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice 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.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, and provision 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 health data using the sensors of the smart glasses 214 or a wearable device. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12. The management unit manages the hormone balance by the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the results managed by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice 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.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, and provision unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user health data using sensors or wearable devices of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The management unit manages hormone balance using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the results managed by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice 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.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user health data using the robot 414's sensors and wearable devices. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The management unit manages hormone balance using the specific processing unit 290 of the data processing unit 12. The provision unit provides the user with the results managed by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A management unit manages hormone balance based on the data analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides the results managed by the aforementioned management unit to the user. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Relaxation Tools The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide an exercise plan The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Offer a meal plan The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide specialized medical consultations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing a community forum The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provides audio guides to promote relaxation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, The system estimates the user's emotions and adjusts the hormone balance management method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, During management, the system analyzes the user's past health data to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, During management, the management methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, During management, the optimal management method is selected considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, During management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, We estimate the user's emotions and adjust the content of the services we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a service, the system analyzes the user's past usage history to select the most suitable service. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the service content will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the services to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we take the user's geographical location into consideration to provide the most suitable service. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose service content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A management unit manages hormone balance based on the data analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides the results managed by the aforementioned management unit to the user. A system characterized by the following features.

2. The aforementioned supply unit is, Relaxation Tools The system according to feature 1.

3. The aforementioned supply unit is, Provide an exercise plan The system according to feature 1.

4. The aforementioned supply unit is, Offer a meal plan The system according to feature 1.

5. The aforementioned supply unit is, We provide specialized medical consultations. The system according to feature 1.

6. The aforementioned supply unit is, Providing a community forum The system according to feature 1.

7. The aforementioned supply unit is, Provides audio guides to promote relaxation. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. 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.

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