system

Abstract

The system according to this embodiment aims to provide total support from before pregnancy through pregnancy and even to postpartum childcare. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a childcare information provision unit. The reception unit inputs user information. The analysis unit analyzes the information input by the reception unit. The provision unit provides information based on the information analyzed by the analysis unit. The childcare information provision unit provides information related to post-pregnancy care and post-natal childcare.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, total support from before pregnancy to after pregnancy and even up to childcare after childbirth is not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide total support from before pregnancy to after pregnancy and even up to childcare after childbirth.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a childcare information provision unit. The reception unit receives user information. The analysis unit analyzes the information received by the reception unit. The provision unit provides information based on the information analyzed by the analysis unit. The childcare information provision unit provides information related to post-pregnancy care and post-natal childcare. [Effects of the Invention]

[0007] The system according to this embodiment can provide total support from before pregnancy, through pregnancy, and even to postpartum childcare. [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 controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[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 total support system according to an embodiment of the present invention is a system that provides total support to couples who want to have children in the future, from before conception to after conception. This total support system allows the user to input information such as age, prefecture, menstrual cycle, and treatment history. Next, the AI ​​analyzes this information and provides information tailored to the user's situation. For example, it provides information on diet, nutrients, and exercise necessary for pregnancy. In the case of infertility treatment, it provides appropriate guidance on when to move to the next step in treatment and offers support methods such as emotional care at the right time. Furthermore, it provides total support regarding post-pregnancy care and postpartum childcare. For example, the user inputs information such as age, prefecture, menstrual cycle, and treatment history. In this case, the user only needs to input detailed information about their situation. For example, they input information such as age, menstrual cycle, and past treatment history. This information is input to the AI. Next, the AI ​​analyzes the input information and provides information tailored to the user's situation. Based on the user's input information, the AI ​​presents information on diet, nutrients, and exercise necessary for pregnancy. For example, if a specific nutrient is deficient, it suggests a recipe for a meal containing that nutrient. It also provides advice on appropriate exercise methods and frequency. Furthermore, in the case of infertility treatment, the AI ​​suggests when to move to the next step. For example, if pregnancy does not occur within a certain period, the system advises on when to proceed to the next treatment step. It also provides information on mental health care and suggests appropriate coping strategies when users are troubled. For post-pregnancy care, the AI ​​provides diet and exercise advice based on the health status of the mother and fetus. For example, it suggests recipes containing nutrients necessary during pregnancy and exercise methods suitable for pregnant women. It also reminds users of necessary medical examinations. The system provides comprehensive support regarding postpartum childcare. For example, it provides information on care methods necessary for the mother's recovery after childbirth and information on managing the baby's health. As a result, users can receive consistent support from before pregnancy, throughout pregnancy, and into postpartum childcare. This system allows users to obtain appropriate information without wasting money or time. It also alleviates anxieties and questions about pregnancy and childcare, allowing users to proceed with pregnancy and childcare with peace of mind.For example, by suggesting recipes containing the nutrients necessary during pregnancy, users can maintain a healthy pregnancy. Furthermore, by providing information on postpartum childcare, users can learn proper parenting methods and protect their baby's health. In this way, the total support system can provide consistent support from before pregnancy, throughout pregnancy, and into postpartum childcare.

[0029] The total support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a childcare information provision unit. The reception unit inputs user information. User information includes, but is not limited to, age, prefecture, menstrual cycle, and treatment history. For example, the reception unit allows users to input information such as age, menstrual cycle, and past treatment history. The analysis unit analyzes the information input by the reception unit. The analysis unit analyzes the user's input information using, for example, AI, and provides information tailored to the user's situation. The analysis unit performs analysis using, for example, data mining, statistical analysis, and machine learning. The provision unit provides information based on the information analyzed by the analysis unit. For example, the provision unit provides information on diet, nutrients, and exercise necessary during pregnancy. For example, if a user is deficient in a particular nutrient, the provision unit suggests a recipe for a meal containing that nutrient. The provision unit also provides advice on appropriate exercise methods and frequency. The childcare information provision unit provides information on post-pregnancy care and postpartum childcare. For example, the childcare information provision unit suggests a recipe for a meal containing nutrients necessary during pregnancy and an exercise method suitable for pregnancy. Furthermore, the childcare information department also provides reminders for necessary medical examinations. For example, it provides information on care methods necessary for the mother's postpartum recovery and information on the baby's health management. As a result, the total support system according to this embodiment can provide consistent support from before pregnancy, through pregnancy, and into postpartum childcare.

[0030] The reception desk inputs user information. This information includes, but is not limited to, age, prefecture, menstrual cycle, and treatment history. For example, the reception desk allows users to input information such as age, menstrual cycle, and past treatment history. Specifically, the reception desk provides an intuitive interface to enable users to easily input information. For example, it provides web forms or applications accessible from smartphones and computers, displaying guides as users enter the necessary information. Furthermore, the entered information is encrypted to ensure security and securely stored in a database. In addition, the reception desk allows users to periodically update the information they have entered, ensuring that the system always maintains the most up-to-date information relevant to the user's situation. For example, when a user updates their menstrual cycle or treatment history, the system records the changes by comparing them with past data and provides this information to the analysis department. The reception desk also creates individual accounts based on the information entered by users, allowing users to access their information at any time. This enables the reception desk to efficiently and securely manage user information and improve the overall reliability of the system.

[0031] The analysis department analyzes the information entered by the reception department. For example, the analysis department uses AI to analyze user input and provide information tailored to the user's situation. Specifically, the analysis department uses technologies such as data mining, statistical analysis, and machine learning for analysis. For instance, the AI ​​assesses the likelihood of pregnancy and health risks based on data such as the user's age, menstrual cycle, and treatment history. Using data mining techniques, it extracts patterns and trends from past data and makes predictions tailored to the user's situation. Statistical analysis is used to compare the user's data with that of other users to identify outliers and risk factors. Machine learning algorithms are used to learn from the user's data and provide personalized advice. For example, the AI ​​analyzes the user's menstrual cycle data to predict ovulation. It also evaluates the effectiveness of specific treatments based on treatment history and proposes future treatment plans. Furthermore, the analysis department continuously monitors user data and updates the analysis results according to changes in the situation. This allows the analysis department to provide optimal information tailored to the user's situation and support their health management.

[0032] The service provider provides information based on data analyzed by the analysis unit. For example, the service provider provides information on diet, nutrients, and exercise necessary for pregnancy. Specifically, the service provider provides customized advice tailored to the user's situation. For example, if a user is deficient in a particular nutrient, the service provider will suggest a recipe for a meal containing that nutrient. If a user has a specific allergy, the service provider will provide a meal plan that addresses that allergy. The service provider also provides advice on appropriate exercise methods and frequency. For example, pregnant users will be suggested light exercises and stretches that can be done within their limits. Furthermore, the service provider provides specific procedures and schedules to make it easier for users to put the suggested information into practice. For example, meal recipes will include detailed information on necessary ingredients and cooking procedures, and exercise plans will show weekly schedules and points to note. In this way, the service provider can provide concrete support for users to lead a healthy life and increase the chances of a successful pregnancy.

[0033] The Childcare Information Department provides information on post-pregnancy care and postpartum childcare. For example, it suggests recipes containing nutrients necessary during pregnancy and exercise methods suitable for pregnant women. Specifically, the Childcare Information Department provides information to help pregnant users maintain their health and support the baby's growth. For example, it suggests recipes containing vitamins and minerals necessary during pregnancy, enabling users to eat a balanced diet. It also suggests exercise methods suitable for pregnant women, enabling users to exercise without overexerting themselves. Furthermore, the Childcare Information Department also provides reminders for necessary medical examinations. For example, it notifies users of the schedule for regular prenatal checkups and ultrasound examinations, enabling them to receive examinations at the appropriate time. After childbirth, it provides information on care methods necessary for maternal recovery and information on baby's health management. For example, it provides advice on diet and exercise to support maternal recovery after childbirth and provides schedules for vaccinations and regular checkups necessary for the baby's health management. In this way, the Childcare Information Department can consistently support users from pregnancy to postpartum, protecting the health of both mother and baby.

[0034] The Step-Up Suggestion Unit can suggest the timing for stepping up infertility treatment. For example, if pregnancy does not occur within a certain period, the Step-Up Suggestion Unit will advise on the timing for moving to the next treatment step. The Step-Up Suggestion Unit can determine the timing of stepping up based on the progress of treatment and the doctor's diagnosis. In this way, it can support the user's treatment by appropriately suggesting the timing of stepping up infertility treatment. Some or all of the above processes in the Step-Up Suggestion Unit may be performed using AI, for example, or not using AI. For example, the Step-Up Suggestion Unit can input the user's treatment history into AI, and the AI ​​can analyze the progress of treatment and the doctor's diagnosis to suggest the timing of stepping up.

[0035] The mental health care service can provide information related to mental health care. For example, it can provide information related to stress management and mental health support. The mental health care service can suggest appropriate solutions when a user is troubled. In this way, it can provide mental support to users by providing information related to mental health care. Some or all of the above processes in the mental health care service may be performed using AI, for example, or not using AI. For example, the mental health care service can input the user's stress level and mental health status into the AI, and the AI ​​can suggest appropriate solutions.

[0036] The reminder unit can provide reminders for necessary medical examinations. For example, the reminder unit can remind users of necessary medical examinations such as blood tests and ultrasound examinations. The reminder unit provides reminders so that users can receive medical examinations at the appropriate time. By providing reminders for necessary medical examinations, the user can receive medical examinations at the appropriate time. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's medical examination schedule into AI, and the AI ​​can provide reminders at the appropriate time.

[0037] The service provider can provide information on diet, nutrients, and exercise necessary for pregnancy. For example, if a user is deficient in a particular nutrient, the service provider can suggest a recipe for a meal containing that nutrient. The service provider can also advise on appropriate exercise methods and frequency. By providing information on diet, nutrients, and exercise necessary for pregnancy, the user can maintain a healthy pregnancy. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's nutritional status and exercise habits into the AI, which can then suggest appropriate diet and exercise methods.

[0038] The childcare information service can provide information on care methods necessary for the mother's postpartum recovery and information on the baby's health management. For example, the service can suggest rest, nutritional management, and exercise as care methods necessary for the mother's postpartum recovery. The service can also provide information on vaccinations, nutritional management, and growth records as information on the baby's health management. By providing information on care methods necessary for the mother's postpartum recovery and information on the baby's health management, users can learn appropriate childcare methods and protect their baby's health. Some or all of the above processing in the childcare information service may be performed using AI, for example, or not. For example, the childcare information service can input the user's health status and childcare history into the AI, which can then suggest appropriate care methods and health management information.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest information that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can suggest the optimal input method.

[0040] The reception desk can customize input fields based on the user's current health and lifestyle when they enter information. For example, if the user is tired, the reception desk will display only the minimum necessary input fields. If the user is in good health, the reception desk will display detailed input fields to collect more accurate information. The reception desk can dynamically change input fields according to the user's lifestyle to collect the most relevant information. This allows for efficient collection of necessary information by customizing input fields according to the user's health and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's health and lifestyle data into the AI, which can then customize the input fields.

[0041] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user lives in a specific region, the reception desk can prioritize inputting information related to that region. If the user is traveling, the reception desk can prioritize inputting information related to their travel destination. If the user is planning to move, the reception desk can prioritize inputting information related to their new residence. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into AI, which can then prioritize inputting highly relevant information.

[0042] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can automatically suggest relevant input fields based on information the user has shared on social media. The reception desk can analyze the user's interests and preferences from their social media activity and prompt them to input relevant information. The reception desk can suggest relevant input fields based on information about accounts the user follows on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into AI, which can then prompt the AI ​​to input relevant information.

[0043] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past data. The analysis unit can extract specific patterns from the user's past data and adjust the analysis algorithm. The analysis unit can analyze the user's past data and improve the accuracy of the analysis algorithm. In this way, by referring to the user's past data, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI, and the AI ​​can select the optimal analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis based on the user's health status and lifestyle during the analysis. For example, the analysis unit adjusts the analysis algorithm considering the user's health status. The analysis unit can improve the accuracy of the analysis based on the user's lifestyle. The analysis unit can reflect the user's health status and lifestyle in real time and optimize the analysis results. This allows for more accurate analysis results by improving the accuracy of the analysis based on the user's health status and lifestyle. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user health status and lifestyle data into the AI, which can then adjust the analysis algorithm.

[0045] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can prioritize analyzing data related to that region. If the user is traveling, the analysis unit can prioritize analyzing data related to the travel destination. If the user is planning to move, the analysis unit can prioritize analyzing data related to the new place of residence. In this way, by taking the user's geographical location information into consideration, the analysis unit can prioritize the analysis of highly relevant 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 geographical location information into AI, and the AI ​​can prioritize the analysis of highly relevant data.

[0046] The analysis unit can improve the accuracy of the analysis by referring to the user's relevant literature during the analysis. For example, the analysis unit can adjust the analysis algorithm based on literature the user has previously referenced. The analysis unit can extract specific patterns from the user's relevant literature and optimize the analysis algorithm. The analysis unit can analyze the user's relevant literature and improve the accuracy of the analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into AI, and the AI ​​can adjust the analysis algorithm.

[0047] The information delivery unit can select the optimal information delivery method by referring to the user's past data when providing information. For example, the information delivery unit can select the optimal information delivery method based on the user's past data. The information delivery unit can extract specific patterns from the user's past data and adjust the information delivery method. The information delivery unit can analyze the user's past data and improve the accuracy of the information delivery method. As a result, by referring to the user's past data, the optimal information delivery method can be selected and the accuracy of information delivery can be improved. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past data into AI, and the AI ​​can select the optimal information delivery method.

[0048] The information provider can adjust the level of detail of the information based on the user's health status and lifestyle when providing information. For example, the information provider can adjust the level of detail of the information considering the user's health status. The information provider can adjust the level of detail of the information based on the user's lifestyle. The information provider can reflect the user's health status and lifestyle in real time and improve the accuracy of information provision. As a result, by adjusting the level of detail of the information based on the user's health status and lifestyle, it is possible to provide the user with the most appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user health status and lifestyle data into AI, and the AI ​​can adjust the level of detail of the information.

[0049] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, if the user lives in a specific area, the provider can prioritize providing information related to that area. If the user is traveling, the provider can prioritize providing information related to their travel destination. If the user is planning to move, the provider can prioritize providing information related to their new place of residence. In this way, by considering the user's geographical location, the provider can prioritize providing highly relevant information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's geographical location information into AI, and the AI ​​can prioritize providing highly relevant information.

[0050] The information provider can analyze a user's social media activity and provide relevant information when providing information. For example, the information provider can automatically provide relevant information based on information shared by the user on social media. The information provider can analyze a user's interests and preferences from their social media activity and provide relevant information. The information provider can provide relevant information based on information about accounts that the user follows on social media. In this way, relevant information can be efficiently provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user social media activity data into AI, and the AI ​​can provide relevant information.

[0051] The childcare information provision unit can provide optimal information by referring to the user's past childcare history when providing childcare information. For example, the childcare information provision unit can provide optimal childcare information based on the childcare methods the user has used in the past. The childcare information provision unit can extract specific patterns from the user's past childcare history and provide childcare information. The childcare information provision unit can analyze the user's past childcare history and improve the accuracy of the childcare information. In this way, by referring to the user's past childcare history, it is possible to provide optimal childcare information and improve the accuracy of the childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or without using AI. For example, the childcare information provision unit can input the user's past childcare history data into AI, and the AI ​​can provide optimal childcare information.

[0052] The childcare information provision unit can adjust the level of detail of information provided based on the user's health status and living situation. For example, the unit can adjust the level of detail of childcare information considering the user's health status. The unit can adjust the level of detail of childcare information based on the user's living situation. The unit can reflect the user's health status and living situation in real time and improve the accuracy of childcare information provision. As a result, by adjusting the level of detail of information based on the user's health status and living situation, it can provide the user with the most suitable childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or without AI. For example, the childcare information provision unit can input the user's health status and living situation data into AI, and the AI ​​can adjust the level of detail of the information.

[0053] The childcare information provision unit can provide optimal information by considering the user's geographical location when providing childcare information. For example, if the user lives in a specific area, the childcare information provision unit can prioritize providing childcare information related to that area. If the user is traveling, the childcare information provision unit can prioritize providing childcare information related to the travel destination. If the user is planning to move, the childcare information provision unit can prioritize providing childcare information related to the new place of residence. In this way, by considering the user's geographical location, it is possible to prioritize providing highly relevant childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or not using AI. For example, the childcare information provision unit can input the user's geographical location information into AI, and the AI ​​can provide highly relevant childcare information.

[0054] The step-up suggestion unit can make optimal suggestions by referring to the user's past treatment history when making step-up suggestions. For example, the step-up suggestion unit can make optimal step-up suggestions based on the user's past treatment history. The step-up suggestion unit can extract specific patterns from the user's past treatment history and make step-up suggestions. The step-up suggestion unit can analyze the user's past treatment history and improve the accuracy of step-up suggestions. As a result, by referring to the user's past treatment history, it can make optimal step-up suggestions and improve the accuracy of the suggestions. Some or all of the above processing in the step-up suggestion unit may be performed using AI, for example, or without using AI. For example, the step-up suggestion unit can input the user's past treatment history data into AI, and the AI ​​can make optimal step-up suggestions.

[0055] The step-up suggestion unit can make optimal suggestions by considering the user's geographical location information when making step-up suggestions. For example, if the user lives in a specific region, the step-up suggestion unit can prioritize step-up suggestions related to that region. If the user is traveling, the step-up suggestion unit can prioritize step-up suggestions related to the travel destination. If the user is planning to move, the step-up suggestion unit can prioritize step-up suggestions related to the new place of residence. In this way, by considering the user's geographical location information, it is possible to prioritize highly relevant step-up suggestions. Some or all of the above processing in the step-up suggestion unit may be performed using AI, for example, or not using AI. For example, the step-up suggestion unit can input the user's geographical location information into AI, and the AI ​​can make highly relevant step-up suggestions.

[0056] The mental health care service can provide optimal information by referring to the user's past psychological state when providing mental health care information. For example, the mental health care service can provide optimal mental health care information based on the user's past psychological state. The mental health care service can extract specific patterns from the user's past psychological state and provide mental health care information. The mental health care service can analyze the user's past psychological state and improve the accuracy of the mental health care information. As a result, by referring to the user's past psychological state, it is possible to provide optimal mental health care information and improve the accuracy of the information. Some or all of the above processing in the mental health care service may be performed using AI, for example, or without using AI. For example, the mental health care service can input the user's past psychological state data into AI, and the AI ​​can provide optimal mental health care information.

[0057] The mental health care service can provide optimal information by considering the user's geographical location when providing mental health care information. For example, if the user lives in a specific area, the service can prioritize providing mental health care information related to that area. If the user is traveling, the service can prioritize providing mental health care information related to their travel destination. If the user is planning to move, the service can prioritize providing mental health care information related to their new place of residence. In this way, by considering the user's geographical location, the service can prioritize providing highly relevant mental health care information. Some or all of the above processing in the mental health care service may be performed using AI, for example, or not. For example, the service can input the user's geographical location information into AI, and the AI ​​can provide highly relevant mental health care information.

[0058] The reminder unit can select the optimal reminder method by referring to the user's past medical history when sending a reminder. For example, the reminder unit can select the optimal reminder method based on the user's past medical history. The reminder unit can extract specific patterns from the user's past medical history and adjust the reminder method. The reminder unit can analyze the user's past medical history and improve the accuracy of the reminder method. As a result, by referring to the user's past medical history, the system can select the optimal reminder method and improve the accuracy of reminders. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past medical history data into AI, which can then select the optimal reminder method.

[0059] The reminder unit can provide the most relevant reminders by considering the user's geographical location. For example, if the user lives in a specific region, the reminder unit can prioritize reminders related to that region. If the user is traveling, the reminder unit can prioritize reminders related to the travel destination. If the user is planning to move, the reminder unit can prioritize reminders related to the new place of residence. In this way, by considering the user's geographical location, the unit can prioritize highly relevant reminders. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location into the AI, which can then provide highly relevant reminders.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Based on the user's past input history, it can predict and suggest information that will be used during specific time periods. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can suggest the optimal input method.

[0062] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, it can select the optimal analysis algorithm based on the user's past data. It can extract specific patterns from the user's past data and adjust the analysis algorithm accordingly. It can analyze the user's past data to improve the accuracy of the analysis algorithm. In this way, by referring to the user's past data, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI, and the AI ​​can select the optimal analysis algorithm.

[0063] The information delivery unit can select the optimal information delivery method by referring to the user's past data when providing information. For example, it can select the optimal information delivery method based on the user's past data. It can extract specific patterns from the user's past data and adjust the information delivery method. It can analyze the user's past data to improve the accuracy of the information delivery method. As a result, by referring to the user's past data, it is possible to select the optimal information delivery method and improve the accuracy of information delivery. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past data into AI, and the AI ​​can select the optimal information delivery method.

[0064] The reminder unit can select the optimal reminder method by referring to the user's past medical history when sending a reminder. For example, it can select the optimal reminder method based on the user's past medical history. It can extract specific patterns from the user's past medical history and adjust the reminder method accordingly. It can analyze the user's past medical history to improve the accuracy of the reminder method. As a result, by referring to the user's past medical history, the optimal reminder method can be selected and the accuracy of the reminder can be improved. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past medical history data into AI, and the AI ​​can select the optimal reminder method.

[0065] The childcare information provision unit can provide optimal information by referring to the user's past childcare history when providing childcare information. For example, it can provide optimal childcare information based on the childcare methods the user has used in the past. It can extract specific patterns from the user's past childcare history and provide childcare information based on those patterns. It can analyze the user's past childcare history to improve the accuracy of childcare information. In this way, by referring to the user's past childcare history, it is possible to provide optimal childcare information and improve the accuracy of childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or not using AI. For example, the childcare information provision unit can input the user's past childcare history data into AI, and the AI ​​can provide optimal childcare information.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk enters the user's information. User information may include, but is not limited to, age, prefecture, menstrual cycle, and treatment history. For example, the reception desk can allow the user to enter information such as age, menstrual cycle, and past treatment history. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the user's input information using, for example, AI, and provides information tailored to the user's situation. The analysis unit performs analysis using, for example, data mining, statistical analysis, and machine learning. Step 3: The information provider provides information based on the data analyzed by the analysis unit. For example, the provider provides information on diet, nutrients, and exercise necessary for pregnancy. For example, if there is a deficiency in a particular nutrient, the provider will suggest a recipe for a meal containing that nutrient. The provider will also provide advice on appropriate exercise methods and frequency. Step 4: The Childcare Information Department provides information on post-pregnancy care and postpartum childcare. For example, the Childcare Information Department suggests recipes containing nutrients necessary during pregnancy and exercise methods suitable for pregnant women. The Childcare Information Department also reminds expectant mothers of necessary medical examinations. For example, the Childcare Information Department provides information on care methods necessary for the mother's postpartum recovery and information on managing the baby's health.

[0068] (Example of form 2) The total support system according to an embodiment of the present invention is a system that provides total support to couples who want to have children in the future, from before conception to after conception. This total support system allows the user to input information such as age, prefecture, menstrual cycle, and treatment history. Next, the AI ​​analyzes this information and provides information tailored to the user's situation. For example, it provides information on diet, nutrients, and exercise necessary for pregnancy. In the case of infertility treatment, it provides appropriate guidance on when to move to the next step in treatment and offers support methods such as emotional care at the right time. Furthermore, it provides total support regarding post-pregnancy care and postpartum childcare. For example, the user inputs information such as age, prefecture, menstrual cycle, and treatment history. In this case, the user only needs to input detailed information about their situation. For example, they input information such as age, menstrual cycle, and past treatment history. This information is input to the AI. Next, the AI ​​analyzes the input information and provides information tailored to the user's situation. Based on the user's input information, the AI ​​presents information on diet, nutrients, and exercise necessary for pregnancy. For example, if a specific nutrient is deficient, it suggests a recipe for a meal containing that nutrient. It also provides advice on appropriate exercise methods and frequency. Furthermore, in the case of infertility treatment, the AI ​​suggests when to move to the next step. For example, if pregnancy does not occur within a certain period, the system advises on when to proceed to the next treatment step. It also provides information on mental health care and suggests appropriate coping strategies when users are troubled. For post-pregnancy care, the AI ​​provides diet and exercise advice based on the health status of the mother and fetus. For example, it suggests recipes containing nutrients necessary during pregnancy and exercise methods suitable for pregnant women. It also reminds users of necessary medical examinations. The system provides comprehensive support regarding postpartum childcare. For example, it provides information on care methods necessary for the mother's recovery after childbirth and information on managing the baby's health. As a result, users can receive consistent support from before pregnancy, throughout pregnancy, and into postpartum childcare. This system allows users to obtain appropriate information without wasting money or time. It also alleviates anxieties and questions about pregnancy and childcare, allowing users to proceed with pregnancy and childcare with peace of mind.For example, by suggesting recipes containing the nutrients necessary during pregnancy, users can maintain a healthy pregnancy. Furthermore, by providing information on postpartum childcare, users can learn proper parenting methods and protect their baby's health. In this way, the total support system can provide consistent support from before pregnancy, throughout pregnancy, and into postpartum childcare.

[0069] The total support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a childcare information provision unit. The reception unit inputs user information. User information includes, but is not limited to, age, prefecture, menstrual cycle, and treatment history. For example, the reception unit allows users to input information such as age, menstrual cycle, and past treatment history. The analysis unit analyzes the information input by the reception unit. The analysis unit analyzes the user's input information using, for example, AI, and provides information tailored to the user's situation. The analysis unit performs analysis using, for example, data mining, statistical analysis, and machine learning. The provision unit provides information based on the information analyzed by the analysis unit. For example, the provision unit provides information on diet, nutrients, and exercise necessary during pregnancy. For example, if a user is deficient in a particular nutrient, the provision unit suggests a recipe for a meal containing that nutrient. The provision unit also provides advice on appropriate exercise methods and frequency. The childcare information provision unit provides information on post-pregnancy care and postpartum childcare. For example, the childcare information provision unit suggests a recipe for a meal containing nutrients necessary during pregnancy and an exercise method suitable for pregnancy. Furthermore, the childcare information department also provides reminders for necessary medical examinations. For example, it provides information on care methods necessary for the mother's postpartum recovery and information on the baby's health management. As a result, the total support system according to this embodiment can provide consistent support from before pregnancy, through pregnancy, and into postpartum childcare.

[0070] The reception desk inputs user information. This information includes, but is not limited to, age, prefecture, menstrual cycle, and treatment history. For example, the reception desk allows users to input information such as age, menstrual cycle, and past treatment history. Specifically, the reception desk provides an intuitive interface to enable users to easily input information. For example, it provides web forms or applications accessible from smartphones and computers, displaying guides as users enter the necessary information. Furthermore, the entered information is encrypted to ensure security and securely stored in a database. In addition, the reception desk allows users to periodically update the information they have entered, ensuring that the system always maintains the most up-to-date information relevant to the user's situation. For example, when a user updates their menstrual cycle or treatment history, the system records the changes by comparing them with past data and provides this information to the analysis department. The reception desk also creates individual accounts based on the information entered by users, allowing users to access their information at any time. This enables the reception desk to efficiently and securely manage user information and improve the overall reliability of the system.

[0071] The analysis department analyzes the information entered by the reception department. For example, the analysis department uses AI to analyze user input and provide information tailored to the user's situation. Specifically, the analysis department uses technologies such as data mining, statistical analysis, and machine learning for analysis. For instance, the AI ​​assesses the likelihood of pregnancy and health risks based on data such as the user's age, menstrual cycle, and treatment history. Using data mining techniques, it extracts patterns and trends from past data and makes predictions tailored to the user's situation. Statistical analysis is used to compare the user's data with that of other users to identify outliers and risk factors. Machine learning algorithms are used to learn from the user's data and provide personalized advice. For example, the AI ​​analyzes the user's menstrual cycle data to predict ovulation. It also evaluates the effectiveness of specific treatments based on treatment history and proposes future treatment plans. Furthermore, the analysis department continuously monitors user data and updates the analysis results according to changes in the situation. This allows the analysis department to provide optimal information tailored to the user's situation and support their health management.

[0072] The service provider provides information based on data analyzed by the analysis unit. For example, the service provider provides information on diet, nutrients, and exercise necessary for pregnancy. Specifically, the service provider provides customized advice tailored to the user's situation. For example, if a user is deficient in a particular nutrient, the service provider will suggest a recipe for a meal containing that nutrient. If a user has a specific allergy, the service provider will provide a meal plan that addresses that allergy. The service provider also provides advice on appropriate exercise methods and frequency. For example, pregnant users will be suggested light exercises and stretches that can be done within their limits. Furthermore, the service provider provides specific procedures and schedules to make it easier for users to put the suggested information into practice. For example, meal recipes will include detailed information on necessary ingredients and cooking procedures, and exercise plans will show weekly schedules and points to note. In this way, the service provider can provide concrete support for users to lead a healthy life and increase the chances of a successful pregnancy.

[0073] The Childcare Information Department provides information on post-pregnancy care and postpartum childcare. For example, it suggests recipes containing nutrients necessary during pregnancy and exercise methods suitable for pregnant women. Specifically, the Childcare Information Department provides information to help pregnant users maintain their health and support the baby's growth. For example, it suggests recipes containing vitamins and minerals necessary during pregnancy, enabling users to eat a balanced diet. It also suggests exercise methods suitable for pregnant women, enabling users to exercise without overexerting themselves. Furthermore, the Childcare Information Department also provides reminders for necessary medical examinations. For example, it notifies users of the schedule for regular prenatal checkups and ultrasound examinations, enabling them to receive examinations at the appropriate time. After childbirth, it provides information on care methods necessary for maternal recovery and information on baby's health management. For example, it provides advice on diet and exercise to support maternal recovery after childbirth and provides schedules for vaccinations and regular checkups necessary for the baby's health management. In this way, the Childcare Information Department can consistently support users from pregnancy to postpartum, protecting the health of both mother and baby.

[0074] The Step-Up Suggestion Unit can suggest the timing for stepping up infertility treatment. For example, if pregnancy does not occur within a certain period, the Step-Up Suggestion Unit will advise on the timing for moving to the next treatment step. The Step-Up Suggestion Unit can determine the timing of stepping up based on the progress of treatment and the doctor's diagnosis. In this way, it can support the user's treatment by appropriately suggesting the timing of stepping up infertility treatment. Some or all of the above processes in the Step-Up Suggestion Unit may be performed using AI, for example, or not using AI. For example, the Step-Up Suggestion Unit can input the user's treatment history into AI, and the AI ​​can analyze the progress of treatment and the doctor's diagnosis to suggest the timing of stepping up.

[0075] The mental health care service can provide information related to mental health care. For example, it can provide information related to stress management and mental health support. The mental health care service can suggest appropriate solutions when a user is troubled. In this way, it can provide mental support to users by providing information related to mental health care. Some or all of the above processes in the mental health care service may be performed using AI, for example, or not using AI. For example, the mental health care service can input the user's stress level and mental health status into the AI, and the AI ​​can suggest appropriate solutions.

[0076] The reminder unit can provide reminders for necessary medical examinations. For example, the reminder unit can remind users of necessary medical examinations such as blood tests and ultrasound examinations. The reminder unit provides reminders so that users can receive medical examinations at the appropriate time. By providing reminders for necessary medical examinations, the user can receive medical examinations at the appropriate time. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's medical examination schedule into AI, and the AI ​​can provide reminders at the appropriate time.

[0077] The service provider can provide information on diet, nutrients, and exercise necessary for pregnancy. For example, if a user is deficient in a particular nutrient, the service provider can suggest a recipe for a meal containing that nutrient. The service provider can also advise on appropriate exercise methods and frequency. By providing information on diet, nutrients, and exercise necessary for pregnancy, the user can maintain a healthy pregnancy. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's nutritional status and exercise habits into the AI, which can then suggest appropriate diet and exercise methods.

[0078] The childcare information service can provide information on care methods necessary for the mother's postpartum recovery and information on the baby's health management. For example, the service can suggest rest, nutritional management, and exercise as care methods necessary for the mother's postpartum recovery. The service can also provide information on vaccinations, nutritional management, and growth records as information on the baby's health management. By providing information on care methods necessary for the mother's postpartum recovery and information on the baby's health management, users can learn appropriate childcare methods and protect their baby's health. Some or all of the above processing in the childcare information service may be performed using AI, for example, or not. For example, the childcare information service can input the user's health status and childcare history into the AI, which can then suggest appropriate care methods and health management information.

[0079] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt the user to input information during a time when they can relax. If the user is relaxed, the reception desk may select the timing to request detailed information. If the user is in a hurry, the reception desk may provide a simplified input form to allow for quick information entry. This allows users to input information without feeling stressed by adjusting the timing of information input according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 using AI. For example, the reception desk may input the user's facial expression data into a generative AI, which can estimate emotions and adjust the timing of information input.

[0080] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest information that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can suggest the optimal input method.

[0081] The reception desk can customize input fields based on the user's current health and lifestyle when they enter information. For example, if the user is tired, the reception desk will display only the minimum necessary input fields. If the user is in good health, the reception desk will display detailed input fields to collect more accurate information. The reception desk can dynamically change input fields according to the user's lifestyle to collect the most relevant information. This allows for efficient collection of necessary information by customizing input fields according to the user's health and lifestyle. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's health and lifestyle data into the AI, which can then customize the input fields.

[0082] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize inputting only important information. If the user is relaxed, the reception desk can prioritize inputting detailed information. If the user is in a hurry, the reception desk can prioritize inputting the most important information. In this way, by determining the priority of the information to be entered according to the user's emotions, important information can be prioritized. 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of the information to be entered.

[0083] The reception desk can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user lives in a specific region, the reception desk can prioritize inputting information related to that region. If the user is traveling, the reception desk can prioritize inputting information related to their travel destination. If the user is planning to move, the reception desk can prioritize inputting information related to their new residence. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into AI, which can then prioritize inputting highly relevant information.

[0084] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can automatically suggest relevant input fields based on information the user has shared on social media. The reception desk can analyze the user's interests and preferences from their social media activity and prompt them to input relevant information. The reception desk can suggest relevant input fields based on information about accounts the user follows on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into AI, which can then prompt the AI ​​to input relevant information.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simplified analysis method. If the user is relaxed, the analysis unit can use a detailed analysis method. If the user is in a hurry, the analysis unit can use an analysis method that provides results quickly. By adjusting the analysis method according to the user's emotions, the analysis unit can provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into a generative AI, which can then estimate emotions and adjust the analysis method.

[0086] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past data. The analysis unit can extract specific patterns from the user's past data and adjust the analysis algorithm. The analysis unit can analyze the user's past data and improve the accuracy of the analysis algorithm. In this way, by referring to the user's past data, the analysis algorithm can be optimized and the analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI, and the AI ​​can select the optimal analysis algorithm.

[0087] The analysis unit can improve the accuracy of the analysis based on the user's health status and lifestyle during the analysis. For example, the analysis unit adjusts the analysis algorithm considering the user's health status. The analysis unit can improve the accuracy of the analysis based on the user's lifestyle. The analysis unit can reflect the user's health status and lifestyle in real time and optimize the analysis results. This allows for more accurate analysis results by improving the accuracy of the analysis based on the user's health status and lifestyle. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user health status and lifestyle data into the AI, which can then adjust the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display method that is easy for the user to understand can be provided. 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 using AI. For example, the analysis unit can input the user's facial expression data into the generative AI, and the generative AI can estimate emotions and adjust the display method of the analysis results.

[0089] The analysis unit can perform analysis while taking into account the user's geographical location information. For example, if the user lives in a specific region, the analysis unit can prioritize analyzing data related to that region. If the user is traveling, the analysis unit can prioritize analyzing data related to the travel destination. If the user is planning to move, the analysis unit can prioritize analyzing data related to the new place of residence. In this way, by taking the user's geographical location information into consideration, the analysis unit can prioritize the analysis of highly relevant 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 geographical location information into AI, and the AI ​​can prioritize the analysis of highly relevant data.

[0090] The analysis unit can improve the accuracy of the analysis by referring to the user's relevant literature during the analysis. For example, the analysis unit can adjust the analysis algorithm based on literature the user has previously referenced. The analysis unit can extract specific patterns from the user's relevant literature and optimize the analysis algorithm. The analysis unit can analyze the user's relevant literature and improve the accuracy of the analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into AI, and the AI ​​can adjust the analysis algorithm.

[0091] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is stressed, the information provider can use a simple and highly visible method of information delivery. If the user is relaxed, the information provider can use a method that includes detailed information. If the user is in a hurry, the information provider can use a method that gets straight to the point. By adjusting the method of information delivery according to the user's emotions, the information provider can provide an information delivery method that is easy for the user to understand. 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 information provider may be performed using AI or not using AI. For example, the information provider can input user facial expression data into a generative AI, which can estimate emotions and adjust the method of information delivery.

[0092] The information delivery unit can select the optimal information delivery method by referring to the user's past data when providing information. For example, the information delivery unit can select the optimal information delivery method based on the user's past data. The information delivery unit can extract specific patterns from the user's past data and adjust the information delivery method. The information delivery unit can analyze the user's past data and improve the accuracy of the information delivery method. As a result, by referring to the user's past data, the optimal information delivery method can be selected and the accuracy of information delivery can be improved. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past data into AI, and the AI ​​can select the optimal information delivery method.

[0093] The information provider can adjust the level of detail of the information based on the user's health status and lifestyle when providing information. For example, the information provider can adjust the level of detail of the information considering the user's health status. The information provider can adjust the level of detail of the information based on the user's lifestyle. The information provider can reflect the user's health status and lifestyle in real time and improve the accuracy of information provision. As a result, by adjusting the level of detail of the information based on the user's health status and lifestyle, it is possible to provide the user with the most appropriate information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user health status and lifestyle data into AI, and the AI ​​can adjust the level of detail of the information.

[0094] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is stressed, the information provider can prioritize providing only important information. If the user is relaxed, the information provider can prioritize providing detailed information. If the user is in a hurry, the information provider can prioritize providing the most important information. In this way, by determining the priority of information provision according to the user's emotions, important information can be provided preferentially. 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of information provision.

[0095] The information provider can provide optimal information by considering the user's geographical location when providing information. For example, if the user lives in a specific area, the provider can prioritize providing information related to that area. If the user is traveling, the provider can prioritize providing information related to their travel destination. If the user is planning to move, the provider can prioritize providing information related to their new place of residence. In this way, by considering the user's geographical location, the provider can prioritize providing highly relevant information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the user's geographical location information into AI, and the AI ​​can prioritize providing highly relevant information.

[0096] The information provider can analyze a user's social media activity and provide relevant information when providing information. For example, the information provider can automatically provide relevant information based on information shared by the user on social media. The information provider can analyze a user's interests and preferences from their social media activity and provide relevant information. The information provider can provide relevant information based on information about accounts that the user follows on social media. In this way, relevant information can be efficiently provided by analyzing the user's social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input user social media activity data into AI, and the AI ​​can provide relevant information.

[0097] The childcare information provision unit can estimate the user's emotions and adjust the method of providing childcare information based on the estimated emotions. For example, if the user is stressed, the childcare information provision unit can use a simple and highly visible method of providing childcare information. If the user is relaxed, the childcare information provision unit can use a method that includes detailed childcare information. If the user is in a hurry, the childcare information provision unit can use a method that gets straight to the point. In this way, by adjusting the method of providing childcare information according to the user's emotions, it is possible to provide childcare information that is easy for the user 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 childcare information provision unit may be performed using AI, for example, or not using AI. For example, the childcare information provision unit can input the user's facial expression data into a generative AI, and the generative AI can estimate emotions and adjust the method of providing childcare information.

[0098] The childcare information provision unit can provide optimal information by referring to the user's past childcare history when providing childcare information. For example, the childcare information provision unit can provide optimal childcare information based on the childcare methods the user has used in the past. The childcare information provision unit can extract specific patterns from the user's past childcare history and provide childcare information. The childcare information provision unit can analyze the user's past childcare history and improve the accuracy of the childcare information. In this way, by referring to the user's past childcare history, it is possible to provide optimal childcare information and improve the accuracy of the childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or without using AI. For example, the childcare information provision unit can input the user's past childcare history data into AI, and the AI ​​can provide optimal childcare information.

[0099] The childcare information provision unit can adjust the level of detail of information provided based on the user's health status and living situation. For example, the unit can adjust the level of detail of childcare information considering the user's health status. The unit can adjust the level of detail of childcare information based on the user's living situation. The unit can reflect the user's health status and living situation in real time and improve the accuracy of childcare information provision. As a result, by adjusting the level of detail of information based on the user's health status and living situation, it can provide the user with the most suitable childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or without AI. For example, the childcare information provision unit can input the user's health status and living situation data into AI, and the AI ​​can adjust the level of detail of the information.

[0100] The childcare information provision unit can estimate the user's emotions and prioritize childcare information based on the estimated emotions. For example, if the user is stressed, the unit will prioritize providing only important childcare information. If the user is relaxed, the unit can prioritize providing detailed childcare information. If the user is in a hurry, the unit can prioritize providing the most important childcare information. In this way, by prioritizing childcare information according to the user's emotions, important childcare information can be provided preferentially. 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 childcare information provision unit may be performed using AI, for example, or without AI. For example, the childcare information provision unit can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of childcare information.

[0101] The childcare information provision unit can provide optimal information by considering the user's geographical location when providing childcare information. For example, if the user lives in a specific area, the childcare information provision unit can prioritize providing childcare information related to that area. If the user is traveling, the childcare information provision unit can prioritize providing childcare information related to the travel destination. If the user is planning to move, the childcare information provision unit can prioritize providing childcare information related to the new place of residence. In this way, by considering the user's geographical location, it is possible to prioritize providing highly relevant childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or not using AI. For example, the childcare information provision unit can input the user's geographical location information into AI, and the AI ​​can provide highly relevant childcare information.

[0102] The step-up suggestion unit can estimate the user's emotions and adjust the timing of the step-up based on the estimated emotions. For example, if the user is stressed, the step-up suggestion unit can delay the timing of the step-up. If the user is relaxed, the step-up suggestion unit can speed up the timing of the step-up. If the user is in a hurry, the step-up suggestion unit can quickly suggest the step-up. In this way, by adjusting the timing of the step-up according to the user's emotions, the system can suggest the step-up at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the step-up suggestion unit may be performed using AI, for example, or not using AI. For example, the step-up suggestion unit can input user facial expression data into the generative AI, which can estimate emotions and adjust the timing of the step-up.

[0103] The step-up suggestion unit can make optimal suggestions by referring to the user's past treatment history when making step-up suggestions. For example, the step-up suggestion unit can make optimal step-up suggestions based on the user's past treatment history. The step-up suggestion unit can extract specific patterns from the user's past treatment history and make step-up suggestions. The step-up suggestion unit can analyze the user's past treatment history and improve the accuracy of step-up suggestions. As a result, by referring to the user's past treatment history, it can make optimal step-up suggestions and improve the accuracy of the suggestions. Some or all of the above processing in the step-up suggestion unit may be performed using AI, for example, or without using AI. For example, the step-up suggestion unit can input the user's past treatment history data into AI, and the AI ​​can make optimal step-up suggestions.

[0104] The step-up suggestion unit can estimate the user's emotions and determine the priority of steps based on the estimated emotions. For example, if the user is stressed, the step-up suggestion unit will prioritize only important step-up suggestions. If the user is relaxed, the step-up suggestion unit will prioritize detailed step-up suggestions. If the user is in a hurry, the step-up suggestion unit will prioritize the most important step-up suggestions. In this way, by determining the priority of steps according to the user's emotions, important step-up suggestions can be prioritized. 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 step-up suggestion unit may be performed using AI or not using AI. For example, the step-up suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of steps.

[0105] The step-up suggestion unit can make optimal suggestions by considering the user's geographical location information when making step-up suggestions. For example, if the user lives in a specific region, the step-up suggestion unit can prioritize step-up suggestions related to that region. If the user is traveling, the step-up suggestion unit can prioritize step-up suggestions related to the travel destination. If the user is planning to move, the step-up suggestion unit can prioritize step-up suggestions related to the new place of residence. In this way, by considering the user's geographical location information, it is possible to prioritize highly relevant step-up suggestions. Some or all of the above processing in the step-up suggestion unit may be performed using AI, for example, or not using AI. For example, the step-up suggestion unit can input the user's geographical location information into AI, and the AI ​​can make highly relevant step-up suggestions.

[0106] The mental health care provider can estimate the user's emotions and adjust the method of providing mental health care information based on the estimated emotions. For example, if the user is stressed, the mental health care provider can use a simple and highly visible method of providing mental health care information. If the user is relaxed, the mental health care provider can use a method that includes detailed mental health care information. If the user is in a hurry, the mental health care provider can use a method that gets straight to the point. In this way, by adjusting the method of providing mental health care information according to the user's emotions, a method of providing mental health care information that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 mental health care provider may be performed using AI, for example, or not using AI. For example, the mental health care provider can input the user's facial expression data into the generative AI, and the generative AI can estimate emotions and adjust the method of providing mental health care information.

[0107] The mental health care service can provide optimal information by referring to the user's past psychological state when providing mental health care information. For example, the mental health care service can provide optimal mental health care information based on the user's past psychological state. The mental health care service can extract specific patterns from the user's past psychological state and provide mental health care information. The mental health care service can analyze the user's past psychological state and improve the accuracy of the mental health care information. As a result, by referring to the user's past psychological state, it is possible to provide optimal mental health care information and improve the accuracy of the information. Some or all of the above processing in the mental health care service may be performed using AI, for example, or without using AI. For example, the mental health care service can input the user's past psychological state data into AI, and the AI ​​can provide optimal mental health care information.

[0108] The mental health care provider can estimate the user's emotions and prioritize mental health care information based on the estimated emotions. For example, if the user is stressed, the mental health care provider can prioritize providing only important mental health care information. If the user is relaxed, the mental health care provider can prioritize providing detailed mental health care information. If the user is in a hurry, the mental health care provider can prioritize providing the most important mental health care information. In this way, by prioritizing mental health care information according to the user's emotions, important mental health care information can be prioritized. 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 mental health care provider may be performed using AI or not using AI. For example, the mental health care provider can input the user's facial expression data into a generative AI, which can estimate emotions and determine the priority of mental health care information.

[0109] The mental health care service can provide optimal information by considering the user's geographical location when providing mental health care information. For example, if the user lives in a specific area, the service can prioritize providing mental health care information related to that area. If the user is traveling, the service can prioritize providing mental health care information related to their travel destination. If the user is planning to move, the service can prioritize providing mental health care information related to their new place of residence. In this way, by considering the user's geographical location, the service can prioritize providing highly relevant mental health care information. Some or all of the above processing in the mental health care service may be performed using AI, for example, or not. For example, the service can input the user's geographical location information into AI, and the AI ​​can provide highly relevant mental health care information.

[0110] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is stressed, the reminder unit can send a reminder during a time when the user can relax. If the user is relaxed, the reminder unit can select a time for a more detailed reminder. If the user is in a hurry, the reminder unit can provide a simplified reminder to quickly deliver the information. By adjusting the timing of reminders according to the user's emotions, reminders can be sent at the optimal time for the user. 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 reminder unit may be performed using AI or not. For example, the reminder unit can input user facial expression data into a generative AI, which can then estimate emotions and adjust the timing of reminders.

[0111] The reminder unit can select the optimal reminder method by referring to the user's past medical history when sending a reminder. For example, the reminder unit can select the optimal reminder method based on the user's past medical history. The reminder unit can extract specific patterns from the user's past medical history and adjust the reminder method. The reminder unit can analyze the user's past medical history and improve the accuracy of the reminder method. As a result, by referring to the user's past medical history, the system can select the optimal reminder method and improve the accuracy of reminders. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past medical history data into AI, which can then select the optimal reminder method.

[0112] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated emotions. For example, if the user is stressed, the reminder unit will prioritize only important reminders. If the user is relaxed, the reminder unit will prioritize detailed reminders. If the user is in a hurry, the reminder unit will prioritize the most important reminders. In this way, important reminders can be prioritized by determining the priority of reminders 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 reminder unit may be performed using AI or not using AI. For example, the reminder unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of reminders.

[0113] The reminder unit can provide the most relevant reminders by considering the user's geographical location. For example, if the user lives in a specific region, the reminder unit can prioritize reminders related to that region. If the user is traveling, the reminder unit can prioritize reminders related to the travel destination. If the user is planning to move, the reminder unit can prioritize reminders related to the new place of residence. In this way, by considering the user's geographical location, the unit can prioritize highly relevant reminders. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's geographical location into the AI, which can then provide highly relevant reminders.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, a simplified analysis method can be used. If the user is relaxed, a detailed analysis method can be used. If the user is in a hurry, an analysis method that provides results quickly can be used. This allows the analysis to be tailored to the user by adjusting the analysis method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 a generative AI, which can then estimate emotions and adjust the analysis method.

[0116] The information delivery unit can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is stressed, a simple and highly visible method of information delivery can be used. If the user is relaxed, a method of delivery including detailed information can be used. If the user is in a hurry, a method of delivery that gets straight to the point can be used. In this way, by adjusting the method of information delivery according to the user's emotions, an information delivery method that is easy for the user to understand 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 information delivery unit may be performed using AI, for example, or not using AI. For example, the information delivery unit can input user facial expression data into a generative AI, and the generative AI can estimate emotions and adjust the method of information delivery.

[0117] The reminder unit can estimate the user's emotions and adjust the timing of reminders based on those emotions. For example, if the user is stressed, the reminder can be sent during a time when the user can relax. If the user is relaxed, the unit can select a time for a more detailed reminder. If the user is in a hurry, a simplified reminder can be sent to provide information quickly. By adjusting the timing of reminders according to the user's emotions, reminders can be sent at the optimal time for the user. 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 reminder unit may be performed using AI or not. For example, the reminder unit can input user facial expression data into a generative AI, which can then estimate the emotions and adjust the timing of the reminder.

[0118] The childcare information provision unit can estimate the user's emotions and adjust the method of providing childcare information based on the estimated emotions. For example, if the user is stressed, a simple and highly visible method of providing childcare information can be used. If the user is relaxed, a method including detailed childcare information can be used. If the user is in a hurry, a method that focuses on the essentials can be used. In this way, by adjusting the method of providing childcare information according to the user's emotions, a method of providing childcare information that is easy for the user to understand 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 childcare information provision unit may be performed using AI, for example, or without AI. For example, the childcare information provision unit can input the user's facial expression data into a generative AI, which can estimate emotions and adjust the method of providing childcare information.

[0119] The step-up suggestion unit can estimate the user's emotions and adjust the timing of the step-up based on the estimated emotions. For example, if the user is stressed, the timing of the step-up can be delayed. If the user is relaxed, the timing of the step-up can be advanced. If the user is in a hurry, the step-up can be suggested quickly. In this way, by adjusting the timing of the step-up according to the user's emotions, the system can suggest the step-up at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 step-up suggestion unit may be performed using AI, for example, or not using AI. For example, the step-up suggestion unit can input the user's facial expression data into the generative AI, which can estimate the emotions and adjust the timing of the step-up.

[0120] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Based on the user's past input history, it can predict and suggest information that will be used during specific time periods. In this way, by analyzing the user's past input history, the optimal input method can be suggested, and the user's input work can be made more efficient. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can suggest the optimal input method.

[0121] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, it can select the optimal analysis algorithm based on the user's past data. It can extract specific patterns from the user's past data and adjust the analysis algorithm accordingly. It can analyze the user's past data to improve the accuracy of the analysis algorithm. In this way, by referring to the user's past data, the analysis algorithm can be optimized and the accuracy of the analysis can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's past data into AI, and the AI ​​can select the optimal analysis algorithm.

[0122] The information delivery unit can select the optimal information delivery method by referring to the user's past data when providing information. For example, it can select the optimal information delivery method based on the user's past data. It can extract specific patterns from the user's past data and adjust the information delivery method. It can analyze the user's past data to improve the accuracy of the information delivery method. As a result, by referring to the user's past data, it is possible to select the optimal information delivery method and improve the accuracy of information delivery. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past data into AI, and the AI ​​can select the optimal information delivery method.

[0123] The reminder unit can select the optimal reminder method by referring to the user's past medical history when sending a reminder. For example, it can select the optimal reminder method based on the user's past medical history. It can extract specific patterns from the user's past medical history and adjust the reminder method accordingly. It can analyze the user's past medical history to improve the accuracy of the reminder method. As a result, by referring to the user's past medical history, the optimal reminder method can be selected and the accuracy of the reminder can be improved. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input the user's past medical history data into AI, and the AI ​​can select the optimal reminder method.

[0124] The childcare information provision unit can provide optimal information by referring to the user's past childcare history when providing childcare information. For example, it can provide optimal childcare information based on the childcare methods the user has used in the past. It can extract specific patterns from the user's past childcare history and provide childcare information based on those patterns. It can analyze the user's past childcare history to improve the accuracy of childcare information. In this way, by referring to the user's past childcare history, it is possible to provide optimal childcare information and improve the accuracy of childcare information. Some or all of the above processing in the childcare information provision unit may be performed using AI, for example, or not using AI. For example, the childcare information provision unit can input the user's past childcare history data into AI, and the AI ​​can provide optimal childcare information.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The reception desk enters the user's information. User information may include, but is not limited to, age, prefecture, menstrual cycle, and treatment history. For example, the reception desk can allow the user to enter information such as age, menstrual cycle, and past treatment history. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the user's input information using, for example, AI, and provides information tailored to the user's situation. The analysis unit performs analysis using, for example, data mining, statistical analysis, and machine learning. Step 3: The information provider provides information based on the data analyzed by the analysis unit. For example, the provider provides information on diet, nutrients, and exercise necessary for pregnancy. For example, if there is a deficiency in a particular nutrient, the provider will suggest a recipe for a meal containing that nutrient. The provider will also provide advice on appropriate exercise methods and frequency. Step 4: The Childcare Information Department provides information on post-pregnancy care and postpartum childcare. For example, the Childcare Information Department suggests recipes containing nutrients necessary during pregnancy and exercise methods suitable for pregnant women. The Childcare Information Department also reminds expectant mothers of necessary medical examinations. For example, the Childcare Information Department provides information on care methods necessary for the mother's postpartum recovery and information on managing the baby's health.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, childcare information provision unit, step-up proposal unit, mental health care provision unit, and reminder unit, is implemented by 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 and inputs user information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information. The provision unit is implemented by the output device 40 of the smart device 14 and provides information based on the analysis results. The childcare information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on post-pregnancy care and post-natal childcare. The step-up proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the timing for stepping up infertility treatment. The mental health care provision unit is implemented by the output device 40 of the smart device 14 and provides information on mental health care. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and reminds users of necessary medical examinations. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, childcare information provision unit, step-up suggestion unit, mental health care provision unit, and reminder unit, is implemented 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 and inputs user information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides information based on the analysis results. The childcare information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on post-pregnancy care and post-natal childcare. The step-up suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the timing for stepping up infertility treatment. The mental health care provision unit is implemented by the speaker 240 of the smart glasses 214 and provides information on mental health care. The reminder function is implemented by the specific processing unit 290 of the data processing device 12, and it provides reminders for necessary medical examinations. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, childcare information provision unit, step-up suggestion unit, mental health care provision unit, and reminder unit, is implemented by 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 and inputs user information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides information based on the analysis results. The childcare information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on post-pregnancy care and post-natal childcare. The step-up suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests the timing for stepping up infertility treatment. The mental health care provision unit is implemented by the speaker 240 of the headset terminal 314 and provides information on mental health care. The reminder function is implemented by the specific processing unit 290 of the data processing device 12, and it provides reminders for necessary medical examinations. The correspondence between each part and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, childcare information provision unit, step-up proposal unit, mental health care provision unit, and reminder 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 and inputs user information. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information. The provision unit is implemented by the speaker 240 of the robot 414 and provides information based on the analysis results. The childcare information provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides information on post-pregnancy care and post-natal childcare. The step-up proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the timing for stepping up infertility treatment. The mental health care provision unit is implemented by the speaker 240 of the robot 414 and provides information on mental health care. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and reminds users of necessary medical examinations. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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."

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] (Note 1) A reception area where user information is entered, An analysis unit analyzes the information input by the reception unit, A providing unit that provides information based on the information analyzed by the aforementioned analysis unit, It includes a childcare information department that provides information on post-pregnancy care and post-natal childcare. A system characterized by the following features. (Note 2) We have a department that proposes the timing for advancing infertility treatments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility includes a mental health care department that provides information on mental health care. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a reminder function to remind you of necessary medical tests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provides information on diet, nutrients, and exercise necessary for pregnancy. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned childcare information provision department, This service provides information on care methods necessary for postpartum maternal recovery and on baby health management. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information, the input fields are customized based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the user's health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing information, the system selects the most suitable method of information delivery by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing information, the level of detail is adjusted based on the user's health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned childcare information provision department, The system estimates the user's emotions and adjusts how childcare information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned childcare information provision department, When providing childcare information, we refer to the user's past childcare history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned childcare information provision department, When providing childcare information, the level of detail is adjusted based on the user's health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned childcare information provision department, It estimates the user's emotions and prioritizes childcare information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned childcare information provision department, When providing childcare information, we will provide the most relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned step-up proposal unit is It estimates the user's emotions and adjusts the timing of step-ups based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned step-up proposal unit is When suggesting a step-up treatment, we refer to the user's past treatment history to make the most appropriate suggestion. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned step-up proposal unit is It estimates the user's emotions and determines the priority of the next steps based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned step-up proposal unit is When proposing a step-up option, we will consider the user's geographical location to provide the most suitable suggestion. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned mental health care provision department, The system estimates the user's emotions and adjusts how mental health care information is provided based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned mental health care provision department, When providing mental health care information, we refer to the user's past psychological state to provide the most appropriate information. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned mental health care provision department, It estimates the user's emotions and prioritizes mental health care information based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned mental health care provision department, When providing mental health care information, we will consider the user's geographical location to provide the most relevant information. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned reminder unit, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned reminder unit, When sending a reminder, the system will refer to the user's past medical history to select the most appropriate reminder method. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned reminder unit, It estimates the user's emotions and determines the priority of reminders based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned reminder unit, When sending reminders, the system will take the user's geographical location into consideration to provide the most appropriate reminder. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where user information is entered, An analysis unit analyzes the information input by the reception unit, A providing unit that provides information based on the information analyzed by the aforementioned analysis unit, It includes a childcare information department that provides information on post-pregnancy care and post-natal childcare. A system characterized by the following features.

2. We have a department that proposes the timing for advancing infertility treatments. The system according to feature 1.

3. The facility includes a mental health care department that provides information on mental health care. The system according to feature 1.

4. It includes a reminder function to remind you of necessary medical tests. The system according to feature 1.

5. The aforementioned supply unit is, Provides information on diet, nutrients, and exercise necessary for pregnancy. The system according to feature 1.

6. The aforementioned childcare information provision department, This service provides information on care methods necessary for postpartum maternal recovery and on baby health management. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering information, the input fields are customized based on the user's current health status and lifestyle. The system according to feature 1.

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