system

Abstract

The system according to this embodiment aims to analyze health data for women who are trying to conceive and provide them with appropriate advice. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a management unit, and an operation unit. The reception unit inputs health data. The analysis unit analyzes the data input by the reception unit. The provision unit provides advice based on the data analyzed by the analysis unit. The management unit manages stress during fertility treatment. The operation unit operates an online community.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, analysis of health data for women during pregnancy and providing appropriate advice have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze health data for women during pregnancy and provide appropriate advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a management unit, and an operation unit. The reception unit inputs health data. The analysis unit analyzes the data input by the reception unit. The provision unit provides advice based on the data analyzed by the analysis unit. The management unit manages stress during fertility treatment. The operation unit manages an online community. [Effects of the Invention]

[0007] The system according to this embodiment can analyze health data for women who are trying to conceive and provide appropriate advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 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 fertility support system according to an embodiment of the present invention is an AI application designed to help couples engage in fertility treatment while deepening their understanding of the process, given the increasing number of couples undergoing fertility treatment due to later marriages. This fertility support system analyzes individual women's health data (menstrual cycle, basal body temperature, hormone levels, etc.) and provides advice on the optimal timing for pregnancy and lifestyle habits. Furthermore, it accurately predicts ovulation days and fertile periods based on past data and provides support for stress management and mental health during fertility treatment. It also provides relaxation and mindfulness techniques and operates an AI-powered online community and forum to enable information exchange and support among people in similar situations. First, the user inputs their own health data. For example, they input data such as menstrual cycle, basal body temperature, and hormone levels. This data is input into the AI. Next, the AI ​​analyzes the input data and provides advice on the optimal timing for pregnancy and lifestyle habits. For example, the AI ​​analyzes menstrual cycle and basal body temperature data to accurately predict ovulation days and fertile periods. This allows the user to receive advice based on their own health condition. Furthermore, the AI ​​also provides support for stress management and mental health during fertility treatment. For example, the system provides relaxation and mindfulness techniques to help users reduce stress. It also operates AI-powered online communities and forums, allowing users to exchange information and receive support from others in similar situations. This enables users to share information and receive support from others. This system can support those struggling with fertility treatments, such as those facing financial difficulties, lack of understanding from their partners, or burnout. For instance, improving lifestyle habits based on AI-provided advice can increase the chances of conception. Furthermore, sharing information and receiving support from others through the online community can reduce stress during fertility treatments. Thus, the fertility support system can analyze users' health data, provide advice on optimal timing and lifestyle habits, manage stress, and operate online communities to support fertility treatments.

[0029] The fertility support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a management unit, and an operation unit. The reception unit receives the user's health data. Health data includes, but is not limited to, menstrual cycle, basal body temperature, and hormone levels. The reception unit allows users to input health data using, for example, a smartphone or personal computer. The reception unit also securely stores the data entered by the user and transmits it to the analysis unit. The analysis unit analyzes the data entered by the reception unit. The analysis unit, for example, uses AI to analyze menstrual cycle and basal body temperature data and predicts ovulation days and fertile periods with high accuracy. The analysis unit, for example, uses machine learning algorithms to learn from past data and predict future ovulation days. The analysis unit also identifies areas for improvement in lifestyle habits based on the user's health data and transmits them to the provision unit. The provision unit provides advice based on the data analyzed by the analysis unit. The provision unit, for example, uses AI to provide the user with advice on the optimal timing for pregnancy and lifestyle habits. The service provider department, for example, offers relaxation and mindfulness techniques to support users in reducing stress. They can also provide diet and exercise advice based on users' health data. The management department provides stress management and mental health support during fertility treatment. For example, the management department uses AI to monitor users' stress levels and provide appropriate relaxation techniques. For example, if a user is feeling stressed, the management department suggests relaxation techniques such as deep breathing or meditation. They can also provide information on counseling and psychotherapy to support users' mental health. The operations department manages online communities. For example, the operations department uses AI to run online communities and forums where people in similar situations can exchange information and support each other. For example, the operations department provides bulletin boards and chat functions so users can share information and receive support from others. They also support users in sharing information and experiences related to fertility treatment and encouraging each other through the online community.As a result, the fertility support system according to this embodiment can support fertility efforts by analyzing the user's health data, providing advice on the optimal timing for pregnancy and lifestyle habits, and managing stress and operating an online community.

[0030] The reception desk receives user data. This data includes, but is not limited to, menstrual cycle, basal body temperature, and hormone levels. Users can input health data using their smartphones or personal computers. Specifically, users can record their daily basal body temperature and input their menstrual cycle in a calendar format through a dedicated application. Hormone level data can be obtained using a home hormone testing kit, and the results can be entered into the application. This allows users to easily collect and input their health data into the system. Furthermore, the reception desk securely stores the data entered by users and transmits it to the analysis department. Data storage is secured using encryption technology to protect user privacy. For example, data is stored on a cloud server and can only be viewed by those with access rights. In addition, SSL / TLS protocol is used to ensure the security of communication during data transmission. This allows the reception desk to securely manage user health data and provide accurate data to the analysis department.

[0031] The analysis unit analyzes the data entered by the reception unit. For example, the analysis unit uses AI to analyze menstrual cycle and basal body temperature data to predict ovulation days and fertile periods with high accuracy. Specifically, the AI ​​uses machine learning algorithms to learn from past data and predict future ovulation days. For example, the user inputs basal body temperature data for the past few months, and the AI ​​analyzes this data to identify ovulation patterns. Hormone level data is also used in the analysis to improve the accuracy of ovulation day and fertile period predictions. Furthermore, the analysis unit identifies areas for improvement in lifestyle habits based on the user's health data and sends this information to the service provider. For example, the AI ​​analyzes the user's diet and exercise data to identify areas for improvement in nutritional balance and exercise volume. This allows the analysis unit to comprehensively evaluate the user's health status and provide specific advice to create an environment conducive to pregnancy.

[0032] The service provider offers advice based on data analyzed by the analysis department. For example, the service provider uses AI to provide users with advice on the optimal timing for conception and lifestyle habits. Specifically, the AI ​​displays the optimal timing for conception in a calendar format based on the user's health data, allowing the user to plan accordingly. In addition, as lifestyle advice, it provides relaxation and mindfulness techniques to support users in reducing stress. For example, it provides guidance on deep breathing exercises and meditation so that users can practice them in their daily lives. Furthermore, the service provider can also provide advice on diet and exercise based on the user's health data. For example, it proposes nutritionally balanced meal menus and exercise plans to create a body more conducive to pregnancy. In this way, the service provider can provide concrete support for users to pursue fertility treatment while leading a healthy lifestyle.

[0033] The management department provides stress management and mental health support during fertility treatment. For example, the management department uses AI to monitor users' stress levels and provide appropriate relaxation techniques. Specifically, the AI ​​analyzes user input data and daily activity data to assess stress levels. For instance, it calculates stress levels based on heart rate, sleep patterns, and daily activity levels, and provides feedback to the user. If the user is feeling stressed, it suggests relaxation techniques such as deep breathing and meditation. Furthermore, the management department can also provide information on counseling and psychotherapy to support users' mental health. For example, it provides an online counseling reservation system and information on psychotherapy so that users can receive the support they need. In this way, the management department can comprehensively support users' mental health and reduce stress during fertility treatment.

[0034] The operations department manages online communities. For example, the operations department operates online communities and forums that use AI to facilitate information exchange and support among people in similar situations. Specifically, it provides bulletin boards and chat functions so that users can share information and receive support from others. For example, users can post questions and concerns about fertility treatment and receive advice and experiences from other users. The operations department also supports users in sharing information and experiences about fertility treatment and encouraging each other through the online community. For example, it regularly holds online events and webinars to provide advice from experts and the latest information. Furthermore, the operations department monitors for troubles and inappropriate posts within the community and takes measures to maintain healthy community management. In this way, the operations department can provide an online community where users can safely exchange information and receive support, and support their fertility treatment.

[0035] The analysis unit can predict ovulation days and fertile periods with high accuracy based on past data. For example, the analysis unit predicts the next ovulation day based on past menstrual cycle data. The analysis unit can also identify ovulation days by analyzing basal body temperature data. Furthermore, the analysis unit can predict fertile periods by analyzing hormone level data. This increases the probability of pregnancy by accurately predicting ovulation days and fertile periods based on past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past menstrual cycle data into a generating AI and have the generating AI predict the next ovulation day.

[0036] The service provider can offer relaxation and mindfulness techniques. For example, it may suggest deep breathing exercises. It can also offer meditation techniques. Furthermore, it can offer body scanning techniques. By providing relaxation and mindfulness techniques, it is possible to reduce stress during fertility treatment. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's stress level into a generating AI and have the generating AI suggest appropriate relaxation techniques.

[0037] The management department can provide stress management and mental health support during fertility treatment. For example, the management department can monitor the user's stress level and provide appropriate relaxation techniques. It can also suggest relaxation techniques such as deep breathing or meditation if the user is experiencing stress. Furthermore, the management department can provide information on counseling and psychotherapy to support the user's mental health. This allows for the maintenance of the user's mental well-being through stress management and mental health support during fertility treatment. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input the user's stress level into a generating AI and have the generating AI suggest appropriate relaxation techniques.

[0038] The operations department can run online communities and forums where people in similar situations can exchange information and receive support. For example, the operations department can provide a bulletin board so that users can share information. They can also provide a chat function so that users can communicate in real time. Furthermore, the operations department can host online events for users to participate in. In this way, by operating online communities and forums, users can share information and receive support from others. Some or all of the above processes performed by the operations department may be carried out using AI, for example, or not. For example, the operations department can input user posts into a generating AI and have the generating AI suggest related topics.

[0039] The reception desk can analyze the user's past health data input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze the user's past input history to see if they tend to input data at specific times of day and suggest the optimal input timing. Furthermore, the reception desk can customize the input form based on the types of data the user has entered in the past. This allows the reception desk to provide the user with the most suitable input method by analyzing their past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0040] The reception unit can filter health data input based on the user's current lifestyle and areas of interest. For example, the reception unit can prioritize inputting health data relevant to the user's current lifestyle (work, family, etc.). The reception unit can also suggest types of data to input based on the user's areas of interest (exercise, diet, etc.). Furthermore, the reception unit can adjust the timing of data input to match the user's daily rhythm. This allows for more relevant data input by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering of relevant data.

[0041] The reception desk can prioritize inputting highly relevant data when users enter health data, taking into account their geographical location. For example, if a user is in a specific region, the reception desk will prioritize inputting data related to the climate and environment of that region. Furthermore, if a user is traveling, the reception desk can adjust the input of health data based on the environment of their travel destination. Additionally, if a user is at home, the reception desk can prioritize inputting data related to their daily life. This allows for the priority input of highly relevant data by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI prioritize relevant data.

[0042] The reception desk can analyze a user's social media activity and input relevant data when entering health data. For example, the reception desk can suggest data to be entered based on health information shared by the user on social media. The reception desk can also infer the user's current health status from their social media activity and adjust the data to be entered accordingly. Furthermore, the reception desk can suggest the types of data to be entered based on health-related accounts that the user follows on social media. This allows for efficient input of relevant data by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on general health data. Furthermore, the analysis unit can perform a detailed analysis on data of high user interest. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0044] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a cycle analysis algorithm to menstrual cycle data. It can also apply a temperature fluctuation analysis algorithm to basal body temperature data. Furthermore, it can apply a hormone balance analysis algorithm to hormone level data. By applying the appropriate analysis algorithm according to the category of health data, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0045] The analysis unit can determine the priority of analysis based on when the health data was submitted. For example, the analysis unit may prioritize the analysis of recently submitted health data. It can also prioritize the analysis of data submitted regularly. Furthermore, the analysis unit may prioritize the analysis of data submitted by the user in relation to a specific event (e.g., ovulation). This allows for the prioritization of the analysis based on when the health data was submitted, ensuring that the most recent data is analyzed first. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI determine the priority of analysis based on the submission date.

[0046] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also prioritize the analysis of data of high user interest. Furthermore, it can prioritize the analysis of data that significantly impacts the user's health status. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0047] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. It can also provide simplified advice for general health data. Furthermore, it can provide detailed advice for data of high interest to the user. This allows for detailed advice to be provided for important data by adjusting the level of detail of advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI adjust the level of detail of the advice based on importance.

[0048] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, for menstrual cycle data, the service provider can provide advice based on the cycle. It can also provide advice based on temperature fluctuations for basal body temperature data. Furthermore, it can provide advice based on hormone balance for hormone level data. This allows for more accurate advice by applying the appropriate advice algorithm according to the category of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI apply the appropriate advice algorithm based on the category.

[0049] The service provider can prioritize advice based on when health data is submitted. For example, it may provide advice based on recently submitted health data. It may also provide advice based on regularly submitted data. Furthermore, it may provide advice based on data submitted by the user in relation to a specific event (e.g., ovulation). This allows for the provision of advice based on the latest data by prioritizing advice based on when health data is submitted. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's health data into a generating AI and have the generating AI determine the priority of advice based on the submission timing.

[0050] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider can provide advice based on highly relevant data. It can also provide advice based on data of high interest to the user. Furthermore, it can provide advice based on data that significantly impacts the user's health status. By adjusting the order of advice based on the relevance of health data, it is possible to provide advice based on highly relevant data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI perform the adjustment of the order of advice based on relevance.

[0051] The management department can analyze a user's past stress data to select the optimal stress management method during stress management. For example, the management department can prioritize suggesting stress management methods that have been effective for the user in the past. Furthermore, the management department can select effective management methods for specific situations based on the user's past stress data. In addition, the management department can analyze the user's past stress data and provide a customized version of the most effective management method. This allows the management department to provide the user with the most suitable stress management method by analyzing past stress 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 input the user's past stress data into a generating AI and have the generating AI select the optimal management method.

[0052] The management unit can customize stress management methods based on the user's current lifestyle. For example, if the user is busy at work, the management unit can provide effective stress management methods in a short amount of time. It can also provide stress management methods suited to the home environment if the user spends a lot of time at home. Furthermore, if the user is traveling, the management unit can provide stress management methods that can be practiced at their travel destination. This allows for more effective stress management by customizing management methods based on the user's lifestyle. Some or all of the above-described processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the management methods.

[0053] The management unit can select the optimal stress management method by considering the user's geographical location information during stress management. For example, if the user is in an urban area, the management unit can provide a stress management method suitable for the urban environment. Furthermore, if the user is in a natural environment, the management unit can provide a stress management method that utilizes nature. Additionally, if the user is traveling, the management unit can provide a stress management method suitable for the travel destination. This allows the management unit to provide the user with the most suitable stress management method by considering geographical location information. 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 unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0054] The management department can analyze a user's social media activity and propose stress management measures during stress management. For example, the management department can propose management measures based on stress-related information shared by the user on social media. The management department can also estimate the user's current stress level from their social media activity and adjust the management measures accordingly. Furthermore, the management department can propose management measures based on stress management-related accounts that the user follows on social media. In this way, by analyzing social media activity, the management department can provide the user with the most suitable stress management measures. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of management measures.

[0055] The operations department can select the optimal operation method by referring to the user's past community activity history when managing the community. For example, the operations department can suggest relevant topics based on the topics the user has previously participated in. The operations department can also prioritize providing topics of interest to the user based on their past activity history. Furthermore, the operations department can analyze the user's past activity history and customize and provide the most effective operation method. In this way, the operations department can provide the user with the optimal operation method by referring to their past community activity history. Some or all of the above processes by the operations department may be performed using AI, for example, or not using AI. For example, the operations department can input the user's past community activity history data into a generating AI and have the generating AI select the optimal operation method.

[0056] The operations team can customize the methods of community management based on the user's current lifestyle. For example, if a user is busy with work, the operations team can provide topics that can be participated in in a short amount of time. If a user spends a lot of time at home, the operations team can also provide topics suitable for their home environment. Furthermore, if a user is traveling, the operations team can provide topics that can be participated in while traveling. This allows for more effective community management by customizing the methods of operation based on the user's lifestyle. Some or all of the above processes performed by the operations team may be carried out using AI, for example, or not. For example, the operations team can input user lifestyle data into a generating AI and have the generating AI perform the customization of the methods of operation.

[0057] The operations department can select the optimal operation method when managing a community, taking into account the user's geographical location information. For example, if the user is in an urban area, the operations department can provide community activities suitable for the urban environment. Furthermore, if the user is in a natural environment, the operations department can provide community activities that utilize nature. Additionally, if the user is traveling, the operations department can provide community activities suitable for the environment of their travel destination. In this way, by considering geographical location information, the operations department can provide the user with the most suitable community operation method. Some or all of the above processing by the operations department may be performed using AI, for example, or without AI. For example, the operations department can input the user's geographical location information into a generating AI and have the generating AI select the optimal operation method.

[0058] The operations department can analyze users' social media activity and propose operational strategies when managing a community. For example, the operations department can propose relevant community activities based on information shared by users on social media. Furthermore, the operations department can infer users' current interests from their social media activity and adjust community activities accordingly. In addition, the operations department can propose relevant community activities based on accounts that users follow on social media. This allows the operations department to provide users with the most suitable community operational strategies by analyzing their social media activity. Some or all of the above processes performed by the operations department may be carried out using AI, for example, or not. For example, the operations department can input user social media activity data into a generating AI and have the generating AI generate suggestions for operational strategies.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The fertility support system can also collect and analyze the health data of the user's partner. For example, by providing advice that takes into account the partner's lifestyle and health condition, more effective fertility support becomes possible. Specifically, it can analyze the partner's diet and exercise habits and suggest improvements to lifestyle habits that are suitable for pregnancy. It can also monitor the partner's stress level and provide appropriate relaxation techniques. Furthermore, it can provide information and educational content to deepen the partner's understanding of fertility. This allows the partner to participate in fertility efforts together, promoting cooperation between the couple.

[0061] Fertility support systems can utilize not only historical data but also real-time data when analyzing users' health data. For example, by incorporating real-time heart rate and body temperature data obtained from wearable devices into the analysis, it becomes possible to predict the timing of conception more accurately. Furthermore, based on real-time data, it is possible to provide immediate advice tailored to the user's current health condition. Moreover, by utilizing real-time data, it is possible to quickly respond to changes in the user's lifestyle and provide appropriate advice. This can increase the success rate of fertility treatment.

[0062] The fertility support system can combine different analytical algorithms when analyzing the user's health data. For example, a cycle analysis algorithm can be applied to menstrual cycle data, and a temperature fluctuation analysis algorithm to basal body temperature data. A hormone balance analysis algorithm can also be applied to hormone level data. Furthermore, these analysis results can be integrated to make a comprehensive prediction of the timing of conception. This allows for more accurate analysis results by applying the appropriate analytical algorithm to different data sets.

[0063] The fertility support system can adjust the level of detail in its analysis of user health data based on the importance of the data. For example, it can perform detailed analysis on important health data, and simplified analysis on general health data. Furthermore, it can perform detailed analysis on data of high user interest. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the health data.

[0064] The fertility support system can prioritize the analysis of user health data based on when the data was submitted. For example, it can prioritize the analysis of recently submitted health data. It can also prioritize the analysis of regularly submitted data. Furthermore, it can prioritize the analysis of data submitted by the user in relation to specific events (e.g., ovulation). By prioritizing the analysis based on when the health data was submitted, the system can ensure that the most recent data is analyzed first.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the user's health data. This data includes menstrual cycle, basal body temperature, hormone levels, etc. Users can enter their health data using a smartphone or personal computer. The reception desk also securely stores the data entered by the user and transmits it to the analysis department. Step 2: The analysis unit analyzes the data entered by the reception unit. The analysis unit uses AI to analyze menstrual cycle and basal body temperature data to predict ovulation days and fertile periods with high accuracy. Furthermore, it uses machine learning algorithms to learn from past data and predict future ovulation days. It also identifies areas for improvement in lifestyle habits based on the user's health data and sends them to the service provider. Step 3: The service provider provides advice based on the data analyzed by the analysis unit. The service provider uses AI to provide users with advice on the optimal timing for pregnancy and lifestyle habits. Furthermore, it provides relaxation and mindfulness techniques to support users in reducing stress. It can also provide diet and exercise advice based on the user's health data. Step 4: The management department provides stress management and mental health support during fertility treatment. The management department uses AI to monitor the user's stress level and provide appropriate relaxation techniques. For example, if the user is feeling stressed, it will suggest relaxation techniques such as deep breathing or meditation. It can also provide information on counseling and psychotherapy to support the user's mental health. Step 5: The management team operates the online community. The management team uses AI to operate online communities and forums where people in similar situations can exchange information and receive support. For example, they provide message boards and chat functions so that users can share information and receive support from others. They also support users in sharing information and experiences related to fertility treatment through the online community and encouraging each other.

[0067] (Example of form 2) The fertility support system according to an embodiment of the present invention is an AI application designed to help couples engage in fertility treatment while deepening their understanding of the process, given the increasing number of couples undergoing fertility treatment due to later marriages. This fertility support system analyzes individual women's health data (menstrual cycle, basal body temperature, hormone levels, etc.) and provides advice on the optimal timing for pregnancy and lifestyle habits. Furthermore, it accurately predicts ovulation days and fertile periods based on past data and provides support for stress management and mental health during fertility treatment. It also provides relaxation and mindfulness techniques and operates an AI-powered online community and forum to enable information exchange and support among people in similar situations. First, the user inputs their own health data. For example, they input data such as menstrual cycle, basal body temperature, and hormone levels. This data is input into the AI. Next, the AI ​​analyzes the input data and provides advice on the optimal timing for pregnancy and lifestyle habits. For example, the AI ​​analyzes menstrual cycle and basal body temperature data to accurately predict ovulation days and fertile periods. This allows the user to receive advice based on their own health condition. Furthermore, the AI ​​also provides support for stress management and mental health during fertility treatment. For example, the system provides relaxation and mindfulness techniques to help users reduce stress. It also operates AI-powered online communities and forums, allowing users to exchange information and receive support from others in similar situations. This enables users to share information and receive support from others. This system can support those struggling with fertility treatments, such as those facing financial difficulties, lack of understanding from their partners, or burnout. For instance, improving lifestyle habits based on AI-provided advice can increase the chances of conception. Furthermore, sharing information and receiving support from others through the online community can reduce stress during fertility treatments. Thus, the fertility support system can analyze users' health data, provide advice on optimal timing and lifestyle habits, manage stress, and operate online communities to support fertility treatments.

[0068] The fertility support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a management unit, and an operation unit. The reception unit receives the user's health data. Health data includes, but is not limited to, menstrual cycle, basal body temperature, and hormone levels. The reception unit allows users to input health data using, for example, a smartphone or personal computer. The reception unit also securely stores the data entered by the user and transmits it to the analysis unit. The analysis unit analyzes the data entered by the reception unit. The analysis unit, for example, uses AI to analyze menstrual cycle and basal body temperature data and predicts ovulation days and fertile periods with high accuracy. The analysis unit, for example, uses machine learning algorithms to learn from past data and predict future ovulation days. The analysis unit also identifies areas for improvement in lifestyle habits based on the user's health data and transmits them to the provision unit. The provision unit provides advice based on the data analyzed by the analysis unit. The provision unit, for example, uses AI to provide the user with advice on the optimal timing for pregnancy and lifestyle habits. The service provider department, for example, offers relaxation and mindfulness techniques to support users in reducing stress. They can also provide diet and exercise advice based on users' health data. The management department provides stress management and mental health support during fertility treatment. For example, the management department uses AI to monitor users' stress levels and provide appropriate relaxation techniques. For example, if a user is feeling stressed, the management department suggests relaxation techniques such as deep breathing or meditation. They can also provide information on counseling and psychotherapy to support users' mental health. The operations department manages online communities. For example, the operations department uses AI to run online communities and forums where people in similar situations can exchange information and support each other. For example, the operations department provides bulletin boards and chat functions so users can share information and receive support from others. They also support users in sharing information and experiences related to fertility treatment and encouraging each other through the online community.As a result, the fertility support system according to this embodiment can support fertility efforts by analyzing the user's health data, providing advice on the optimal timing for pregnancy and lifestyle habits, and managing stress and operating an online community.

[0069] The reception desk receives user data. This data includes, but is not limited to, menstrual cycle, basal body temperature, and hormone levels. Users can input health data using their smartphones or personal computers. Specifically, users can record their daily basal body temperature and input their menstrual cycle in a calendar format through a dedicated application. Hormone level data can be obtained using a home hormone testing kit, and the results can be entered into the application. This allows users to easily collect and input their health data into the system. Furthermore, the reception desk securely stores the data entered by users and transmits it to the analysis department. Data storage is secured using encryption technology to protect user privacy. For example, data is stored on a cloud server and can only be viewed by those with access rights. In addition, SSL / TLS protocol is used to ensure the security of communication during data transmission. This allows the reception desk to securely manage user health data and provide accurate data to the analysis department.

[0070] The analysis unit analyzes the data entered by the reception unit. For example, the analysis unit uses AI to analyze menstrual cycle and basal body temperature data to predict ovulation days and fertile periods with high accuracy. Specifically, the AI ​​uses machine learning algorithms to learn from past data and predict future ovulation days. For example, the user inputs basal body temperature data for the past few months, and the AI ​​analyzes this data to identify ovulation patterns. Hormone level data is also used in the analysis to improve the accuracy of ovulation day and fertile period predictions. Furthermore, the analysis unit identifies areas for improvement in lifestyle habits based on the user's health data and sends this information to the service provider. For example, the AI ​​analyzes the user's diet and exercise data to identify areas for improvement in nutritional balance and exercise volume. This allows the analysis unit to comprehensively evaluate the user's health status and provide specific advice to create an environment conducive to pregnancy.

[0071] The service provider offers advice based on data analyzed by the analysis department. For example, the service provider uses AI to provide users with advice on the optimal timing for conception and lifestyle habits. Specifically, the AI ​​displays the optimal timing for conception in a calendar format based on the user's health data, allowing the user to plan accordingly. In addition, as lifestyle advice, it provides relaxation and mindfulness techniques to support users in reducing stress. For example, it provides guidance on deep breathing exercises and meditation so that users can practice them in their daily lives. Furthermore, the service provider can also provide advice on diet and exercise based on the user's health data. For example, it proposes nutritionally balanced meal menus and exercise plans to create a body more conducive to pregnancy. In this way, the service provider can provide concrete support for users to pursue fertility treatment while leading a healthy lifestyle.

[0072] The management department provides stress management and mental health support during fertility treatment. For example, the management department uses AI to monitor users' stress levels and provide appropriate relaxation techniques. Specifically, the AI ​​analyzes user input data and daily activity data to assess stress levels. For instance, it calculates stress levels based on heart rate, sleep patterns, and daily activity levels, and provides feedback to the user. If the user is feeling stressed, it suggests relaxation techniques such as deep breathing and meditation. Furthermore, the management department can also provide information on counseling and psychotherapy to support users' mental health. For example, it provides an online counseling reservation system and information on psychotherapy so that users can receive the support they need. In this way, the management department can comprehensively support users' mental health and reduce stress during fertility treatment.

[0073] The operations department manages online communities. For example, the operations department operates online communities and forums that use AI to facilitate information exchange and support among people in similar situations. Specifically, it provides bulletin boards and chat functions so that users can share information and receive support from others. For example, users can post questions and concerns about fertility treatment and receive advice and experiences from other users. The operations department also supports users in sharing information and experiences about fertility treatment and encouraging each other through the online community. For example, it regularly holds online events and webinars to provide advice from experts and the latest information. Furthermore, the operations department monitors for troubles and inappropriate posts within the community and takes measures to maintain healthy community management. In this way, the operations department can provide an online community where users can safely exchange information and receive support, and support their fertility treatment.

[0074] The analysis unit can predict ovulation days and fertile periods with high accuracy based on past data. For example, the analysis unit predicts the next ovulation day based on past menstrual cycle data. The analysis unit can also identify ovulation days by analyzing basal body temperature data. Furthermore, the analysis unit can predict fertile periods by analyzing hormone level data. This increases the probability of pregnancy by accurately predicting ovulation days and fertile periods based on past data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past menstrual cycle data into a generating AI and have the generating AI predict the next ovulation day.

[0075] The service provider can offer relaxation and mindfulness techniques. For example, it may suggest deep breathing exercises. It can also offer meditation techniques. Furthermore, it can offer body scanning techniques. By providing relaxation and mindfulness techniques, it is possible to reduce stress during fertility treatment. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's stress level into a generating AI and have the generating AI suggest appropriate relaxation techniques.

[0076] The management department can provide stress management and mental health support during fertility treatment. For example, the management department can monitor the user's stress level and provide appropriate relaxation techniques. It can also suggest relaxation techniques such as deep breathing or meditation if the user is experiencing stress. Furthermore, the management department can provide information on counseling and psychotherapy to support the user's mental health. This allows for the maintenance of the user's mental well-being through stress management and mental health support during fertility treatment. Some or all of the above processes performed by the management department may be carried out using AI, for example, or not. For example, the management department can input the user's stress level into a generating AI and have the generating AI suggest appropriate relaxation techniques.

[0077] The operations department can run online communities and forums where people in similar situations can exchange information and receive support. For example, the operations department can provide a bulletin board so that users can share information. They can also provide a chat function so that users can communicate in real time. Furthermore, the operations department can host online events for users to participate in. In this way, by operating online communities and forums, users can share information and receive support from others. Some or all of the above processes performed by the operations department may be carried out using AI, for example, or not. For example, the operations department can input user posts into a generating AI and have the generating AI suggest related topics.

[0078] The reception desk can estimate the user's emotions and adjust the timing of health data entry based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to enter health data during a time when they can relax. The reception desk may also prompt the user to enter detailed health data if they are relaxed. Furthermore, if the user is busy, the reception desk may provide a simplified input form to allow for quick data entry. This allows for more appropriate data entry by adjusting the timing of health data entry 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example.

[0079] The reception desk can analyze the user's past health data input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze the user's past input history to see if they tend to input data at specific times of day and suggest the optimal input timing. Furthermore, the reception desk can customize the input form based on the types of data the user has entered in the past. This allows the reception desk to provide the user with the most suitable input method by analyzing their past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0080] The reception unit can filter health data input based on the user's current lifestyle and areas of interest. For example, the reception unit can prioritize inputting health data relevant to the user's current lifestyle (work, family, etc.). The reception unit can also suggest types of data to input based on the user's areas of interest (exercise, diet, etc.). Furthermore, the reception unit can adjust the timing of data input to match the user's daily rhythm. This allows for more relevant data input by filtering data based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering of relevant data.

[0081] The reception desk can estimate the user's emotions and prioritize the health data to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize stress-related data entry. It can also prompt the user to enter detailed health data if they are relaxed. Furthermore, if the user is tired, the reception desk can prompt them to enter only the most important data. This allows for the priority of important data entry by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can prioritize inputting highly relevant data when users enter health data, taking into account their geographical location. For example, if a user is in a specific region, the reception desk will prioritize inputting data related to the climate and environment of that region. Furthermore, if a user is traveling, the reception desk can adjust the input of health data based on the environment of their travel destination. Additionally, if a user is at home, the reception desk can prioritize inputting data related to their daily life. This allows for the priority input of highly relevant data by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI prioritize relevant data.

[0083] The reception desk can analyze a user's social media activity and input relevant data when entering health data. For example, the reception desk can suggest data to be entered based on health information shared by the user on social media. The reception desk can also infer the user's current health status from their social media activity and adjust the data to be entered accordingly. Furthermore, the reception desk can suggest the types of data to be entered based on health-related accounts that the user follows on social media. This allows for efficient input of relevant data by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit will perform a detailed analysis on important health data. The analysis unit can also perform a simplified analysis on general health data. Furthermore, the analysis unit can perform a detailed analysis on data of high user interest. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0086] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a cycle analysis algorithm to menstrual cycle data. It can also apply a temperature fluctuation analysis algorithm to basal body temperature data. Furthermore, it can apply a hormone balance analysis algorithm to hormone level data. By applying the appropriate analysis algorithm according to the category of health data, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can determine the priority of analysis based on when the health data was submitted. For example, the analysis unit may prioritize the analysis of recently submitted health data. It can also prioritize the analysis of data submitted regularly. Furthermore, the analysis unit may prioritize the analysis of data submitted by the user in relation to a specific event (e.g., ovulation). This allows for the prioritization of the analysis based on when the health data was submitted, ensuring that the most recent data is analyzed first. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI determine the priority of analysis based on the submission date.

[0089] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also prioritize the analysis of data of high user interest. Furthermore, it can prioritize the analysis of data that significantly impacts the user's health status. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's health data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0090] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple, visually easy-to-understand advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is in a hurry, the service provider can provide concise, to-the-point advice. By adjusting the way advice is presented according to the user's emotions, more easily understandable advice can be provided. 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, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice for important health data. It can also provide simplified advice for general health data. Furthermore, it can provide detailed advice for data of high interest to the user. This allows for detailed advice to be provided for important data by adjusting the level of detail of advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI adjust the level of detail of the advice based on importance.

[0092] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, for menstrual cycle data, the service provider can provide advice based on the cycle. It can also provide advice based on temperature fluctuations for basal body temperature data. Furthermore, it can provide advice based on hormone balance for hormone level data. This allows for more accurate advice by applying the appropriate advice algorithm according to the category of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI apply the appropriate advice algorithm based on the category.

[0093] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is excited, the service provider can provide visually stimulating advice. By adjusting the length of the advice according to the user's emotions, the service provider can provide the most appropriate advice for the user. Emotion estimation is achieved using an emotion estimation function, for example, with 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 or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The service provider can prioritize advice based on when health data is submitted. For example, it may provide advice based on recently submitted health data. It may also provide advice based on regularly submitted data. Furthermore, it may provide advice based on data submitted by the user in relation to a specific event (e.g., ovulation). This allows for the provision of advice based on the latest data by prioritizing advice based on when health data is submitted. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's health data into a generating AI and have the generating AI determine the priority of advice based on the submission timing.

[0095] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider can provide advice based on highly relevant data. It can also provide advice based on data of high interest to the user. Furthermore, it can provide advice based on data that significantly impacts the user's health status. By adjusting the order of advice based on the relevance of health data, it is possible to provide advice based on highly relevant data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI perform the adjustment of the order of advice based on relevance.

[0096] The management unit can estimate the user's emotions and adjust stress management methods based on the estimated emotions. For example, if the user is feeling stressed, the management unit can provide relaxation techniques. It can also provide mindfulness techniques if the user is relaxed. Furthermore, if the user is tired, the management unit can provide simple stress management methods. This allows for more effective stress management by adjusting stress management methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, or not. For example, the management unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The management department can analyze a user's past stress data to select the optimal stress management method during stress management. For example, the management department can prioritize suggesting stress management methods that have been effective for the user in the past. Furthermore, the management department can select effective management methods for specific situations based on the user's past stress data. In addition, the management department can analyze the user's past stress data and provide a customized version of the most effective management method. This allows the management department to provide the user with the most suitable stress management method by analyzing past stress 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 input the user's past stress data into a generating AI and have the generating AI select the optimal management method.

[0098] The management unit can customize stress management methods based on the user's current lifestyle. For example, if the user is busy at work, the management unit can provide effective stress management methods in a short amount of time. It can also provide stress management methods suited to the home environment if the user spends a lot of time at home. Furthermore, if the user is traveling, the management unit can provide stress management methods that can be practiced at their travel destination. This allows for more effective stress management by customizing management methods based on the user's lifestyle. Some or all of the above-described processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the management methods.

[0099] The management department can estimate the user's emotions and determine stress management priorities based on the estimated emotions. For example, if the user is experiencing high stress, the management department will prioritize stress management. If the user is experiencing moderate stress, the management department can also perform stress management in parallel with other health management measures. Furthermore, if the user is experiencing low stress, the management department can suggest preventative stress management. This allows for more effective stress management by determining stress management priorities 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 management department may be performed using AI, or not using AI. For example, the management department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The management unit can select the optimal stress management method by considering the user's geographical location information during stress management. For example, if the user is in an urban area, the management unit can provide a stress management method suitable for the urban environment. Furthermore, if the user is in a natural environment, the management unit can provide a stress management method that utilizes nature. Additionally, if the user is traveling, the management unit can provide a stress management method suitable for the travel destination. This allows the management unit to provide the user with the most suitable stress management method by considering geographical location information. 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 unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal management method.

[0101] The management department can analyze a user's social media activity and propose stress management measures during stress management. For example, the management department can propose management measures based on stress-related information shared by the user on social media. The management department can also estimate the user's current stress level from their social media activity and adjust the management measures accordingly. Furthermore, the management department can propose management measures based on stress management-related accounts that the user follows on social media. In this way, by analyzing social media activity, the management department can provide the user with the most suitable stress management measures. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of management measures.

[0102] The operations team can estimate users' emotions and adjust how the community is run based on those estimated emotions. For example, if a user is stressed, the operations team might prioritize providing topics related to relaxation. If a user is relaxed, the operations team might also provide topics that encourage active interaction. Furthermore, if a user is tired, the operations team might provide topics that are easy to participate in. By adjusting how the community is run according to users' emotions, more effective community management becomes possible. 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 by the operations team may be performed using AI, or not using AI. For example, the operations team can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The operations department can select the optimal operation method by referring to the user's past community activity history when managing the community. For example, the operations department can suggest relevant topics based on the topics the user has previously participated in. The operations department can also prioritize providing topics of interest to the user based on their past activity history. Furthermore, the operations department can analyze the user's past activity history and customize and provide the most effective operation method. In this way, the operations department can provide the user with the optimal operation method by referring to their past community activity history. Some or all of the above processes by the operations department may be performed using AI, for example, or not using AI. For example, the operations department can input the user's past community activity history data into a generating AI and have the generating AI select the optimal operation method.

[0104] The operations team can customize the methods of community management based on the user's current lifestyle. For example, if a user is busy with work, the operations team can provide topics that can be participated in in a short amount of time. If a user spends a lot of time at home, the operations team can also provide topics suitable for their home environment. Furthermore, if a user is traveling, the operations team can provide topics that can be participated in while traveling. This allows for more effective community management by customizing the methods of operation based on the user's lifestyle. Some or all of the above processes performed by the operations team may be carried out using AI, for example, or not. For example, the operations team can input user lifestyle data into a generating AI and have the generating AI perform the customization of the methods of operation.

[0105] The operations department can estimate users' emotions and prioritize communities based on those estimated emotions. For example, if a user is experiencing high stress, the operations department will prioritize stress management communities. If a user is experiencing moderate stress, the operations department can also manage communities related to stress management alongside other health management activities. Furthermore, if a user is experiencing low stress, the operations department can suggest preventative community activities. This allows for more effective community management by prioritizing communities according to users' emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing by the operations department may be performed using AI or not. For example, the operations department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0106] The operations department can select the optimal operation method when managing a community, taking into account the user's geographical location information. For example, if the user is in an urban area, the operations department can provide community activities suitable for the urban environment. Furthermore, if the user is in a natural environment, the operations department can provide community activities that utilize nature. Additionally, if the user is traveling, the operations department can provide community activities suitable for the environment of their travel destination. In this way, by considering geographical location information, the operations department can provide the user with the most suitable community operation method. Some or all of the above processing by the operations department may be performed using AI, for example, or without AI. For example, the operations department can input the user's geographical location information into a generating AI and have the generating AI select the optimal operation method.

[0107] The operations department can analyze users' social media activity and propose operational strategies when managing a community. For example, the operations department can propose relevant community activities based on information shared by users on social media. Furthermore, the operations department can infer users' current interests from their social media activity and adjust community activities accordingly. In addition, the operations department can propose relevant community activities based on accounts that users follow on social media. This allows the operations department to provide users with the most suitable community operational strategies by analyzing their social media activity. Some or all of the above processes performed by the operations department may be carried out using AI, for example, or not. For example, the operations department can input user social media activity data into a generating AI and have the generating AI generate suggestions for operational strategies.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The fertility support system can also collect and analyze the health data of the user's partner. For example, by providing advice that takes into account the partner's lifestyle and health condition, more effective fertility support becomes possible. Specifically, it can analyze the partner's diet and exercise habits and suggest improvements to lifestyle habits that are suitable for pregnancy. It can also monitor the partner's stress level and provide appropriate relaxation techniques. Furthermore, it can provide information and educational content to deepen the partner's understanding of fertility. This allows the partner to participate in fertility efforts together, promoting cooperation between the couple.

[0110] The fertility support system can estimate the user's emotions and adjust the advice based on those emotions. For example, if the user is feeling stressed, it will provide advice focused on stress reduction. If the user is relaxed, it can provide more detailed fertility information and techniques. Furthermore, if the user is feeling anxious, it can provide encouraging messages to reassure them. By providing appropriate advice tailored to the user's emotions, the system can enhance the effectiveness of fertility treatment.

[0111] Fertility support systems can utilize not only historical data but also real-time data when analyzing users' health data. For example, by incorporating real-time heart rate and body temperature data obtained from wearable devices into the analysis, it becomes possible to predict the timing of conception more accurately. Furthermore, based on real-time data, it is possible to provide immediate advice tailored to the user's current health condition. Moreover, by utilizing real-time data, it is possible to quickly respond to changes in the user's lifestyle and provide appropriate advice. This can increase the success rate of fertility treatment.

[0112] The fertility support system can estimate the user's emotions and adjust how the online community is run based on those estimates. For example, if a user is feeling stressed, it can prioritize providing topics related to relaxation. If the user is relaxed, it can provide topics that encourage active interaction. Furthermore, if the user is tired, it can provide topics that are easy to participate in. By adjusting the community's operation according to the user's emotions, it becomes possible to manage the community more effectively.

[0113] The fertility support system can combine different analytical algorithms when analyzing the user's health data. For example, a cycle analysis algorithm can be applied to menstrual cycle data, and a temperature fluctuation analysis algorithm to basal body temperature data. A hormone balance analysis algorithm can also be applied to hormone level data. Furthermore, these analysis results can be integrated to make a comprehensive prediction of the timing of conception. This allows for more accurate analysis results by applying the appropriate analytical algorithm to different data sets.

[0114] The fertility support system can estimate the user's emotions and adjust stress management methods based on those estimates. For example, if the user is feeling stressed, it can offer relaxation techniques. If the user is relaxed, it can also offer mindfulness techniques. Furthermore, if the user is tired, it can offer simple stress management methods. By adjusting stress management methods according to the user's emotions, more effective stress management becomes possible.

[0115] The fertility support system can adjust the level of detail in its analysis of user health data based on the importance of the data. For example, it can perform detailed analysis on important health data, and simplified analysis on general health data. Furthermore, it can perform detailed analysis on data of high user interest. This allows for detailed analysis of important data by adjusting the level of detail based on the importance of the health data.

[0116] The fertility support system can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is stressed, it can provide simple, visually easy-to-understand advice. If the user is relaxed, it can provide more detailed advice. Furthermore, if the user is in a hurry, it can provide concise advice that gets straight to the point. In this way, by adjusting the way advice is presented according to the user's emotions, it can provide advice that is easier to understand.

[0117] The fertility support system can prioritize the analysis of user health data based on when the data was submitted. For example, it can prioritize the analysis of recently submitted health data. It can also prioritize the analysis of regularly submitted data. Furthermore, it can prioritize the analysis of data submitted by the user in relation to specific events (e.g., ovulation). By prioritizing the analysis based on when the health data was submitted, the system can ensure that the most recent data is analyzed first.

[0118] The fertility support system can estimate the user's emotions and adjust the length of advice based on those emotions. For example, if the user is in a hurry, it can provide short, concise advice. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is excited, it can provide visually stimulating advice. By adjusting the length of advice according to the user's emotions, it can provide the most appropriate advice for the user.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives the user's health data. This data includes menstrual cycle, basal body temperature, hormone levels, etc. Users can enter their health data using a smartphone or personal computer. The reception desk also securely stores the data entered by the user and transmits it to the analysis department. Step 2: The analysis unit analyzes the data entered by the reception unit. The analysis unit uses AI to analyze menstrual cycle and basal body temperature data to predict ovulation days and fertile periods with high accuracy. Furthermore, it uses machine learning algorithms to learn from past data and predict future ovulation days. It also identifies areas for improvement in lifestyle habits based on the user's health data and sends them to the service provider. Step 3: The service provider provides advice based on the data analyzed by the analysis unit. The service provider uses AI to provide users with advice on the optimal timing for pregnancy and lifestyle habits. Furthermore, it provides relaxation and mindfulness techniques to support users in reducing stress. It can also provide diet and exercise advice based on the user's health data. Step 4: The management department provides stress management and mental health support during fertility treatment. The management department uses AI to monitor the user's stress level and provide appropriate relaxation techniques. For example, if the user is feeling stressed, it will suggest relaxation techniques such as deep breathing or meditation. It can also provide information on counseling and psychotherapy to support the user's mental health. Step 5: The management team operates the online community. The management team uses AI to operate online communities and forums where people in similar situations can exchange information and receive support. For example, they provide message boards and chat functions so that users can share information and receive support from others. They also support users in sharing information and experiences related to fertility treatment through the online community and encouraging each other.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, management unit, and operation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing users to input health data using a smartphone or personal computer. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze menstrual cycle and basal body temperature data and predict ovulation days and fertile periods with high accuracy. The provision unit is implemented by, for example, the control unit 46A of the smart device 14, which provides users with advice on the optimal timing for pregnancy and lifestyle habits. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the user's stress level and provides appropriate relaxation techniques. The operation unit is implemented by, for example, the control unit 46A of the smart device 14, which operates online communities and forums, allowing users to share information with others and receive support. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, management unit, and operation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing users to input health data using the smart glasses. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze menstrual cycle and basal body temperature data and predict ovulation days and fertile periods with high accuracy. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides users with advice on the optimal timing for pregnancy and lifestyle habits. The management unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which monitors the user's stress level and provides appropriate relaxation techniques. The operation unit is implemented, for example, by the control unit 46A of the smart glasses 214, which operates online communities and forums, allowing users to share information and receive support from others. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, management unit, and operation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing users to input health data using the headset terminal. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which uses AI to analyze menstrual cycle and basal body temperature data and predict ovulation days and fertile periods with high accuracy. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314, which provides users with advice on the optimal timing for pregnancy and lifestyle habits. The management unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which monitors the user's stress level and provides appropriate relaxation techniques. The operation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which operates online communities and forums, allowing users to share information and receive support from others. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, management unit, and operation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing users to input health data using the robot. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze menstrual cycle and basal body temperature data and predict ovulation days and fertile periods with high accuracy. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which provides users with advice on the optimal timing for pregnancy and lifestyle habits. The management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which monitors the user's stress level and provides appropriate relaxation techniques. The operation unit is implemented by, for example, the control unit 46A of the robot 414, which operates online communities and forums, allowing users to share information and receive support from others. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The reception area where health data is entered, An analysis unit analyzes the data input by the reception unit, A provisioning unit that provides advice based on the data analyzed by the aforementioned analysis unit, The management department handles stress management during fertility treatment, It includes an operations department that manages the online community, A system characterized by the following features. (Note 2) The aforementioned analysis unit, Predicting ovulation days and fertile periods with high accuracy based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We offer relaxation and mindfulness techniques. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, We provide stress management and mental health support for women trying to conceive. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned operations department, They operate online communities and forums where people in similar situations can exchange information and provide support. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of health data entry based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past health data entry history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering health data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input health data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering health data, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering health data, the system analyzes the user's social media activity and inputs relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on when the health data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the health data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, It estimates the user's emotions and adjusts stress management methods based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned management department, During stress management, the system analyzes the user's past stress data to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, When managing stress, customize the management methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It estimates the user's emotions and determines stress management priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing stress, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing stress, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned operations department, It estimates user sentiment and adjusts community management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned operations department, When managing a community, refer to the user's past community activity history to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned operations department, When managing a community, customize the management methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned operations department, It estimates user sentiment and determines community priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned operations department, When managing a community, select the optimal management method by considering the users' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned operations department, When managing a community, we analyze users' social media activity and propose management strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 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. The reception area where health data is entered, An analysis unit analyzes the data input by the reception unit, A provisioning unit that provides advice based on the data analyzed by the aforementioned analysis unit, The management department handles stress management during fertility treatment, It includes an operations department that manages the online community, A system characterized by the following features.

2. The aforementioned analysis unit, Predicting ovulation days and fertile periods with high accuracy based on past data. The system according to feature 1.

3. The aforementioned supply unit is, We offer relaxation and mindfulness techniques. The system according to feature 1.

4. The aforementioned management department, We provide stress management and mental health support for women trying to conceive. The system according to feature 1.

5. The aforementioned operations department, They operate online communities and forums where people in similar situations can exchange information and provide support. The system according to feature 1.

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

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

8. The aforementioned reception unit is When entering health data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input health data based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When entering health data, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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