System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving data on a subject's lifestyle pattern and health condition; means for generating support information on pregnancy and childbirth based on the data; and means for transmitting the support information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Families expecting their first pregnancy often have many questions and uncertainties, and do not have time to read the guidebook provided by city hall, so they do not know where to start. In particular, there is a need for solutions to a wide range of issues, such as measures to deal with morning sickness, applying for subsidies, balancing work and pregnancy, selecting a hospital for delivery, health management, storing ultrasound images, and consultations regarding pregnancy and childbirth. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving data on a subject's lifestyle patterns and health status, a means for generating support information related to pregnancy and childbirth based on the data, and a means for transmitting the support information. This system makes it possible to consistently provide optimal pregnancy and childbirth support tailored to each individual's circumstances. Specifically, the system provides comprehensive support to families expecting their first pregnancy by providing advice on how to deal with morning sickness and support information on procedures for applying for subsidies.

[0006] "Subject" is a term that refers to a family or individual who is expecting their first pregnancy.

[0007] "Lifestyle patterns" is a term that refers to the behaviors and habits of a subject in their daily life, such as eating, sleeping, and activity time.

[0008] "Health status" is a term that refers to indicators of a subject's physical condition and health, such as weight, blood pressure, and whether or not they have morning sickness.

[0009] "Means for receiving data" is a term that refers to an interface for obtaining information on lifestyle patterns and health conditions from a user.

[0010] "Means for generating support information" is a term used to refer to algorithms or systems that generate advice or information related to pregnancy and childbirth support based on the data received.

[0011] "Means for transmitting assistance information" is a term that refers to notifications, messaging systems, or other communication methods for providing generated assistance information to subjects. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0013] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0016] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0017] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0018] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 1, a 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.

[0023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0024] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0025] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0032] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0033] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments of the system are described below.

[0034] 1. Entering information about the subject and receiving data

[0035] The user uses a dedicated application or web portal to enter data on their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and the device then sends this information to the server.

[0036] Examples:

[0037] 1. The user enters the time and type of breakfast they had into the food diary within the application.

[0038] 2. The terminal sends the entered data to the server and stores it in the database.

[0039] 2. Data analysis and generation of supporting information

[0040] Based on the data received by the server, support information related to pregnancy and childbirth is generated, including information on morning sickness, health management advice, and how to apply for subsidies.

[0041] Examples:

[0042] 1. The server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness.

[0043] 2. The server advises the user on the appropriate pace of weight gain based on the weight data entered by the user.

[0044] 3. Sending and displaying support information

[0045] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[0046] Examples:

[0047] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification.

[0048] 2. The user opens the application and views the provided advice.

[0049] Details of each function of the system

[0050] Morning sickness prevention advice

[0051] The server analyzes the user's lifestyle patterns and provides advice on diet and lifestyle changes to alleviate morning sickness, such as adjusting the timing and content of meals and suggesting relaxation methods.

[0052] Examples:

[0053] 1. The user enters the meal times and contents for the day.

[0054] 2. The server uses that information to send you a notification recommending a lighter meal at your next meal.

[0055] Subsidy application support

[0056] The server provides information about available grants based on the user's situation and guides them through the application process, providing a checklist of required documents and deadlines for submission.

[0057] Examples:

[0058] 1. The user enters their address and number of weeks pregnant.

[0059] 2. The server searches for local grant information and sends notifications guiding users on how to apply for relevant grants.

[0060] health care

[0061] The server analyzes weight and blood pressure data entered periodically, and if any of the data deviates from the normal range, it will issue a warning to the user and recommend that they visit an appropriate medical institution.

[0062] Examples:

[0063] 1. The user enters their daily weight and blood pressure.

[0064] 2. The server detects an abnormal value and notifies the user to seek medical attention immediately.

[0065] Storage and sharing of ultrasound images

[0066] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[0067] Examples:

[0068] 1. The user uploads their most recent ultrasound photo to the app.

[0069] 2. The server organizes the photos and generates links for family members to share them.

[0070] Communication Features

[0071] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI to provide appropriate advice and information, creating a situation where users can receive support at any time.

[0072] Examples:

[0073] 1. A user asks within the app, "What should I do if I have severe morning sickness?"

[0074] 2. The server uses an AI engine to analyze the question and respond with advice such as, "Make sure to drink plenty of water and eat light meals."

[0075] The above is an embodiment of the present invention. This system makes it possible to consistently provide a wide range of support to families expecting their first pregnancy.

[0076] The processing flow will be explained below.

[0077] 1. Server-based support for the grant application process

[0078] Step 1:

[0079] The user enters their address and number of weeks pregnant into a dedicated form, and the device sends the entered information to the server.

[0080] Step 2:

[0081] The server receives the submitted information and searches the database for a list of grants based on the region and gestational age.

[0082] Step 3:

[0083] The server obtains detailed information about the subsidy (eligibility criteria, deadline, required documents, etc.) and notifies the user.

[0084] Step 4:

[0085] The user scans and uploads the necessary documents, and the device sends the uploaded documents to the server.

[0086] Step 5:

[0087] The server uses OCR technology to read the document and check the requirements. If there are any errors, the server notifies the user and requests them to make corrections.

[0088] Step 6:

[0089] After verifying that all required documents are received, the server initiates the application process, submits the application to the designated government agency, and notifies the user of successful submission.

[0090] 2. Advice for dealing with morning sickness

[0091] Step 1:

[0092] The user inputs their lifestyle patterns (meal times, stress levels, daily activities), and the device sends the input information to the server.

[0093] Step 2:

[0094] Based on the information sent, the server searches the database for individually appropriate morning sickness prevention advice and optimizes it using an algorithm.

[0095] Step 3:

[0096] The server generates the advice and notifies the user.

[0097] Step 4:

[0098] The user puts the provided advice into practice and provides feedback on the results (whether it was effective or not). The device then sends the feedback information to the server.

[0099] Step 5:

[0100] The server analyzes the feedback information and updates and adjusts the advice as needed. The server notifies the user of new advice on a weekly basis.

[0101] 3. Support for balancing work and family life

[0102] Step 1:

[0103] The user enters information about their work duties, working hours, and the size of the company. The device then sends the input data to the server.

[0104] Step 2:

[0105] Based on the information entered, the server searches a database for information on industry-specific countermeasures and legal regulations.

[0106] Step 3:

[0107] The server provides users with information on maternity and childcare leave systems and workplace solutions.

[0108] Step 4:

[0109] If a user needs tips on communication in the workplace or advice on time management, the user inputs this information. The terminal then sends the input data to the server.

[0110] Step 5:

[0111] The server generates appropriate advice and notifies the user.

[0112] 4. Gathering information on delivery hospitals and support in selecting them

[0113] Step 1:

[0114] The user inputs the conditions of the hospital they want to visit (whether they offer painless childbirth, the facilities, etc.). The terminal sends the input data to the server.

[0115] Step 2:

[0116] The server searches a database of relevant hospitals in the area based on the entered criteria.

[0117] Step 3:

[0118] The server generates a list of candidate hospitals and provides detailed information to the user.

[0119] Step 4:

[0120] The user browses the detailed information of the candidate hospitals and makes a selection. The terminal sends the selection information to the server.

[0121] Step 5:

[0122] The server saves the selected hospital in the user's favorites list and notifies them of the latest information.

[0123] 5. Health management data entry and abnormal value detection

[0124] Step 1:

[0125] The user periodically inputs weight and blood pressure measurement data into the app, and the device sends the input data to the server.

[0126] Step 2:

[0127] The server compares and analyzes the received data with past data.

[0128] Step 3:

[0129] If the server detects an abnormal value, it generates a warning and notifies the user.

[0130] Step 4:

[0131] The user receives a notification of an abnormal value and inputs a request for information on how to respond. The device sends the input data to the server.

[0132] Step 5:

[0133] The server searches for nearby medical institutions and provides a list of appropriate medical institutions.

[0134] 6. Digital storage and sharing of ultrasound images

[0135] Step 1:

[0136] The user scans the Echo photo and uploads it using the device, which then sends the uploaded photo to the server.

[0137] Step 2:

[0138] The server organizes the photos by date and displays them in an album format.

[0139] Step 3:

[0140] The user selects who (family, friends) they want to share the photos with, and the device sends the selection information to the server.

[0141] Step 4:

[0142] The server generates a shared link and notifies the specified recipient.

[0143] 7. Consultation function as a communication partner

[0144] Step 1:

[0145] The user inputs a question into the chatbot within the app, and the device sends the input data to the server.

[0146] Step 2:

[0147] The server uses an AI engine to analyze the question and find the appropriate answer.

[0148] Step 3:

[0149] The server notifies the user of the generated answer.

[0150] Step 4:

[0151] The user enters an additional question, and the device sends the input data to the server again.

[0152] Step 5:

[0153] The server reparses and generates successively appropriate answers.

[0154] Example 1

[0155] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0156] Families expecting their first pregnancy need information and support regarding pregnancy and childbirth, but there are limited ways to receive appropriate advice and support. In particular, important information such as measures to combat morning sickness and how to apply for subsidies needs to be tailored to each individual's situation. For this reason, a system that can provide efficient and comprehensive support is needed.

[0157] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0158] In this invention, the server includes a means for inputting data on the lifestyle patterns and health conditions of the subject, a means for transmitting the data from a terminal to the server, a server equipped with a data analysis means for generating support information related to pregnancy and childbirth based on the data, and a means for transmitting the support information from the server to the terminal and notifying the user. This makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[0159] "Subject's lifestyle patterns" refers to the totality of an individual's daily behaviors and habits, including meal times, sleep times, and daily activity status.

[0160] "Health status" refers to data that indicates an individual's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[0161] "Means for inputting data" refers to the means by which users input information about their lifestyle patterns and health status, such as using a dedicated application or web portal.

[0162] "Means for transmitting data from a terminal to a server" refers to a communication means for transmitting data from a terminal (e.g., smartphone, PC) to a server via the Internet.

[0163] "Data analysis means" refers to algorithms and software that analyze and process data in order to generate support information related to pregnancy and childbirth based on the received data.

[0164] "Support information" refers to a comprehensive range of information related to pregnancy and childbirth, such as measures to prevent morning sickness, health management advice, and how to apply for subsidies.

[0165] "Server" refers to a computer system for storing data, analyzing data, generating and transmitting support information.

[0166] "Terminal" refers to a device used by a user to input and receive data, such as a smartphone or computer.

[0167] "Means of notification" refers to methods such as push notification, email, or in-app notification for notifying the user of the generated support information.

[0168] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments are described below.

[0169] First, users access a dedicated application or web portal and enter data about their lifestyle and health conditions, including meal times, sleep time, daily activity levels, weight, blood pressure, and whether they experience morning sickness. This data is entered from the user's smartphone, computer, or other device.

[0170] Next, the terminal sends the input data to a server via the Internet. At this time, the data is sent safely using a secure communication method. The server receives the data and stores it in a database.

[0171] The server analyzes the received data using a generative AI model (e.g., GPT-3 or BERT) to generate personalized support information based on the user's lifestyle and health status. This support information covers a wide range of topics, including advice on morning sickness, health management, and how to apply for subsidies.

[0172] As a specific example, if a user inputs the time and content of their breakfast, such as "I had toast and orange juice at 7:30," the server will analyze that information and generate specific dietary advice to alleviate morning sickness, such as "I recommend having some easily digestible fruit and light protein for your next breakfast."

[0173] The generated support information is sent from the server to the user's device. Notifications can be sent in various ways, such as push notifications, emails, and in-app notifications. For example, advice on how to deal with morning sickness can be sent as a push notification, and when the user taps the notification to open the application, detailed advice will be displayed.

[0174] An example of a prompt sentence would be, "What should I do if I have severe morning sickness?" In this case, the server will analyze it using an AI engine and respond with specific advice such as, "Try to drink plenty of fluids and eat light meals."

[0175] This system makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[0176] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0177] Step 1:

[0178] Users access a dedicated application or web portal and enter their lifestyle patterns and health status. Data entered includes meal times, meal contents, sleep time, daily activity status, weight, blood pressure, and whether or not they have morning sickness. For example, users enter information such as "I had toast and orange juice for breakfast at 7:30." This information is saved on the device as initial data.

[0179] Step 2:

[0180] The device sends the entered data to the server. The data is sent via the Internet using a secure communication method. Specifically, the data sent includes the meal details and time entered by the user, and the information "I had toast and orange juice at 7:30" is transferred. The server receives this data and stores it in a database.

[0181] Step 3:

[0182] The server analyzes the received data. Based on the received data, a generative AI model (e.g., GPT-3 or BERT) is used to understand the user's lifestyle patterns and health status. Here, data from the past few weeks is statistically analyzed to identify trends such as "irregular breakfast times." This allows for a deeper understanding of the user's lifestyle patterns.

[0183] Step 4:

[0184] The server generates support information for pregnancy and childbirth based on the analysis results. Based on the lifestyle patterns and health condition data obtained from the analysis, the generative AI model generates appropriate advice and support information. For example, the generated advice might be, "We recommend that you eat easily digestible fruit and light protein for your next breakfast." The generated support information is temporarily stored on the server.

[0185] Step 5:

[0186] The server sends the generated support information to the user's device. Notification methods can be selected from a variety of methods, including push notifications, emails, and in-app notifications. A specific example of how this works is a notification such as, "To alleviate morning sickness, we recommend eating easily digestible fruit and light protein for breakfast."

[0187] Step 6:

[0188] The user receives a notification on their device and opens the app or web portal to view the support information. When the user taps the notification on their smartphone, detailed advice is displayed within the app, allowing the user to review the support information provided and incorporate it into their daily life.

[0189] Through the above processing steps, efficient and timely support information is provided to families expecting their first pregnancy.

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] Families expecting their first pregnancy need comprehensive support regarding pregnancy and childbirth, but current systems lack information tailored to each individual user. Furthermore, there is no mechanism in place to provide the necessary content in a timely manner, making it difficult for information recipients to receive the necessary support at the appropriate time. This creates a problem in which users are unable to feel at ease during their pregnancy.

[0193] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0194] In this invention, the server includes a means for receiving data on the lifestyle patterns and health conditions of a subject, a means for generating support information related to pregnancy and childbirth based on the data, a means for generating the support information as content suitable for each individual, and a means for delivering the content to the user, thereby enabling personalized content optimized for each individual user to be delivered in a timely manner.

[0195] "Covered Person" refers to an individual or family member who needs assistance with pregnancy and childbirth.

[0196] "Lifestyle patterns" refers to the subject's daily habits and behaviors, such as meal times, sleep times, and activity levels.

[0197] "Health status" refers to the subject's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[0198] "Data receiving means" refers to a device or method for acquiring data on the subject's lifestyle patterns and health status and inputting it into the system.

[0199] "Support information generation means" refers to a device or method that generates appropriate advice and information regarding pregnancy and childbirth based on the received data.

[0200] The "content generation means" refers to a device or method for creating the generated support information as content in a form optimized for each individual target person.

[0201] "Content distribution means" refers to a device or method for distributing generated content to a target person in a timely manner.

[0202] "Morning sickness advice" refers to information that includes specific suggestions for dietary and lifestyle changes to reduce morning sickness symptoms.

[0203] "Subsidy application support information" refers to details of subsidies available to eligible persons and support information regarding the application procedures.

[0204] MODE FOR CARRYING OUT THE INVENTION

[0205] The present invention provides a system for providing comprehensive support for pregnancy and childbirth to a subject. Specific embodiments of the system are described below.

[0206] The system has the following main functions:

[0207] 1. Entering information about the subject and receiving data

[0208] Users enter data about their lifestyle and health status via a dedicated application or web portal, including meal times, sleep time, activity level, weight, blood pressure, and whether they suffer from morning sickness. The data entered by the user is sent to a server via the device, where it is stored in the system's central database.

[0209] As a specific example, when a user enters the time and content of breakfast into an application, the device sends the information to a server and stores it in a database.

[0210] 2. Data analysis and generation of supporting information

[0211] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes advice on how to deal with morning sickness, health management, and assistance with applying for subsidies. Furthermore, the support information is generated as personalized content and provided in the most appropriate format for each individual user.

[0212] As a specific example, the server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness. It also advises the user on the appropriate pace of weight gain based on their weight data.

[0213] 3. Sending and displaying support information

[0214] The server notifies the user of the generated assistance information and displays it through the application or web portal. Notifications can be made via push notifications, emails, or in-application notifications. The user can then use their device to review the received assistance information and adjust their actions accordingly.

[0215] For example, the server generates morning sickness advice and sends it to the user's smartphone as a push notification. When the user opens the application, they can view the advice.

[0216] Hardware and software used

[0217] Smartphone: Used as a device for entering information and receiving notifications.

[0218] Server: Used as a backend server for data analysis, support information generation, and notification distribution. For example, AWS or Google Cloud Platform can be used.

[0219] AI engines: Used for data analysis and content generation, specifically generative AI models such as TensorFlow and PyTorch.

[0220] Examples of specific examples and prompts

[0221] Below are examples of specific prompt sentences to be fed into the generative AI model.

[0222] Analyze the user's lifestyle and health data to generate recommended content for the day, such as dietary advice to alleviate morning sickness or exercise advice for weight management.

[0223] By using such prompts, the AI ​​engine can generate appropriate support information and provide it to the user in a personalized format, enabling the system to provide the necessary information at the appropriate time during pregnancy.

[0224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0225] Step 1:

[0226] A user uses a dedicated application or web portal to input data about their lifestyle and health status (e.g., meal times, sleep time, activity level, weight, blood pressure, presence or absence of morning sickness). The input data is received by the device. The input in this step is data about the user's lifestyle and health status, and the output is data stored in the device.

[0227] Step 2:

[0228] The terminal sends the data entered by the user to the server. Specifically, the terminal sends the data to the specified endpoint via the network. The input is the data stored on the terminal, and the output is the data received by the server.

[0229] Step 3:

[0230] The server analyzes the data it receives. Specifically, the server uses an AI engine to generate support information for pregnancy and childbirth based on the user's lifestyle patterns and health status data. At this time, personalized content is generated by a generative AI model. The input is the data received by the server, and the output is the generated support information.

[0231] Step 4:

[0232] Based on the support information generated by the server, the server generates optimal content for each individual user. Specifically, content such as advice on how to deal with morning sickness and health management is created based on the results obtained from the AI ​​engine. The input is analyzed data, and the output is personalized content.

[0233] Step 5:

[0234] The server notifies the user of the generated content. Specifically, the server sends a push notification, email, or in-app notification to the device. The input is the generated content, and the output is the notification to the user.

[0235] Step 6:

[0236] The user receives a notification and opens an application or web portal to view supporting information. Specifically, the user reviews the content within the app and adjusts their behavior accordingly. The input is the received notification and the output is the user action.

[0237] In this way, the entire system works together to provide users with comprehensive and personalized support regarding pregnancy and childbirth.

[0238] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0239] This invention is a system that provides comprehensive support regarding pregnancy and childbirth to families expecting their first pregnancy, and by combining it with an emotion engine that recognizes the user's emotions, it can provide even more comprehensive support.

[0240] 1. Entering information about the subject and receiving data

[0241] The user uses a dedicated application or web portal to input their lifestyle patterns (e.g., meal times, sleep times, daily activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biometric sensor data, etc.). The device then sends this information to the server.

[0242] Examples:

[0243] 1. The user enters the time and contents of breakfast into the food diary within the application, and the emotion engine reports via voice input that they feel "tired."

[0244] 2. The device sends the input data and voice data to the server and stores them in a database.

[0245] 2. Data analysis and generation of supporting information

[0246] Based on the data received by the server, it generates support information related to pregnancy and childbirth. Support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[0247] Examples:

[0248] 1. The server analyzes the user's eating patterns and emotional data and generates dietary advice to alleviate morning sickness. If the user feels "fatigue," the server generates advice that takes this into account.

[0249] 2. The server advises the user on the appropriate pace of weight gain based on the weight and emotional data entered by the user. If the user feels "anxious," it also suggests ways to relax.

[0250] 3. Sending and displaying support information

[0251] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[0252] Examples:

[0253] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification. If the user feels "fatigue," it also sends a notification encouraging them to take a rest.

[0254] 2. The user opens the application and views the provided advice.

[0255] More morning sickness advice

[0256] The server and emotion engine work together to analyze the user's lifestyle patterns and emotional data, and provide advice on dietary and lifestyle changes to alleviate morning sickness.

[0257] Examples:

[0258] 1. The user inputs the times and contents of meals they ate each day, as well as the emotions they felt.

[0259] 2. Based on this information, the server and emotion engine will recommend a lighter meal for the next meal and, if the user feels "tired," will also suggest taking time to rest.

[0260] Subsidy application support details

[0261] The server and emotion engine work together to provide applicable grant information and guide the application process based on the user's situation and emotion data. It also provides a checklist of required documents and deadlines for submission. If the user feels anxious or stressed, it also provides psychological support information.

[0262] Examples:

[0263] 1. The user enters their address, how many weeks pregnant they are, and how they feel.

[0264] 2. The server and emotion engine search for local subsidy information and guide users on how to apply for the appropriate subsidy, while also providing psychological support information if the user feels "anxious."

[0265] Health Management Details

[0266] The server analyzes weight and blood pressure data entered periodically and issues a warning if the data falls outside the normal range. Furthermore, the emotion engine analyzes the emotional data and suggests appropriate relaxation methods or visiting a medical institution if the user is feeling anxious or stressed.

[0267] Examples:

[0268] 1. The user enters their daily weight, blood pressure, and emotional data.

[0269] 2. The server and emotion engine detect abnormal values, notify the user to seek medical attention immediately, and suggest relaxation methods.

[0270] Learn more about storing and sharing ultrasound photos

[0271] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[0272] Examples:

[0273] 1. The user uploads their most recent ultrasound photo to the app.

[0274] 2. The server organizes the photos and generates links for family members to share them.

[0275] Details of the consultation function as a communication partner

[0276] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI and an emotion engine to provide appropriate advice and information, thereby providing even greater support.

[0277] Examples:

[0278] 1. When users ask questions in the app such as "What should I do if I have severe morning sickness?", they also input the emotions they are feeling.

[0279] 2. The server and emotion engine analyze the question and respond with advice such as "Make sure to drink plenty of water and eat light meals," and if the user is "worried," it also suggests further ways to relax.

[0280] The above is a detailed embodiment for carrying out the present invention. By utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be realized for families experiencing their first pregnancy.

[0281] The processing flow will be explained below.

[0282] Processing flow of a system that combines emotion engines

[0283] 1. Information input and data reception

[0284] Step 1:

[0285] Users enter their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness) into a dedicated application or web portal, along with emotional data (e.g., text input, voice input, biometric sensor data, etc.).

[0286] Step 2:

[0287] The terminal transmits the information entered by the user to the server.

[0288] Step 3:

[0289] The server stores the received information in a database, and sends the emotion data to the emotion engine.

[0290] 2. Data analysis and generation of supporting information

[0291] Step 1:

[0292] The server analyzes the stored lifestyle patterns, health status, and emotional data.

[0293] Step 2:

[0294] The emotion engine analyzes the emotion data and identifies the user's current emotional state (e.g., "tired," "anxious," "stressed").

[0295] Step 3:

[0296] The server generates support information for pregnancy and childbirth based on the analysis results, and also reflects the analysis results of the emotion engine.

[0297] 3. Sending and displaying support information

[0298] Step 1:

[0299] The server notifies the user of the assistance information generated by the server via push notification, email, or in-app notification.

[0300] Step 2:

[0301] The user opens the application and views the notified support information.

[0302] Specific function processing flow

[0303] Morning sickness prevention advice

[0304] Step 1:

[0305] Users enter the time and content of their meals throughout the day, as well as the emotions they are feeling, into the app.

[0306] Step 2:

[0307] The device sends input data and emotion data to the server.

[0308] Step 3:

[0309] The server analyzes the data and generates dietary advice to alleviate morning sickness.

[0310] Step 4:

[0311] The emotion engine analyzes the emotional data and, if the user is feeling "tired," generates advice that takes that state into account.

[0312] Step 5:

[0313] The server notifies the user of the generated advice.

[0314] Subsidy application support

[0315] Step 1:

[0316] The user enters their address, how many weeks pregnant they are, and how they are feeling.

[0317] Step 2:

[0318] The terminal transmits the input data and emotion data to the server.

[0319] Step 3:

[0320] The server searches for local grant information and generates notifications guiding users on how to apply for the appropriate grant.

[0321] Step 4:

[0322] The emotion engine analyzes the user's emotional data, and if the user is feeling "anxiety," it also generates mental support information that takes that state into account.

[0323] Step 5:

[0324] The server generates grant application guides and provides moral support information to the user.

[0325] Health management data analysis and outlier detection

[0326] Step 1:

[0327] Users periodically enter their weight, blood pressure, and emotional data into the app.

[0328] Step 2:

[0329] The terminal sends the input data to the server.

[0330] Step 3:

[0331] The server analyzes the data and generates alerts if outliers are detected.

[0332] Step 4:

[0333] The emotion engine analyzes the emotional data and, if the user is feeling "worried" or "stressed," suggests relaxation methods or visiting a medical institution that take that state into consideration.

[0334] Step 5:

[0335] The server will warn the user about the abnormal value and inform them how to relax.

[0336] Storage and sharing of ultrasound images

[0337] Step 1:

[0338] The user scans the ultrasound photo and uploads it using the device.

[0339] Step 2:

[0340] The device sends the uploaded photos to the server.

[0341] Step 3:

[0342] The server organizes the photos by date and displays them in an album format.

[0343] Step 4:

[0344] The user selects who (family, friends) they want to share the photos with.

[0345] Step 5:

[0346] The terminal transmits the selection information to the server.

[0347] Step 6:

[0348] The server generates a shared link and notifies the specified recipient.

[0349] Consultation function as a communication partner

[0350] Step 1:

[0351] Users input questions into the in-app chatbot, along with their emotional state.

[0352] Step 2:

[0353] The terminal sends the input data to the server.

[0354] Step 3:

[0355] The server uses an AI engine and an emotion engine to analyze the question and emotion data and search for the appropriate answer.

[0356] Step 4:

[0357] The server notifies the user of the generated answer, along with additional advice based on the user's emotional state.

[0358] Step 5:

[0359] The user enters a follow-up question.

[0360] Step 6:

[0361] The terminal again sends the input data to the server.

[0362] Step 7:

[0363] The server re-parses and subsequently generates the appropriate answer and notifies the user.

[0364] The above is a specific implementation of a system that combines an emotion engine. This processing flow allows users to receive more personalized support for pregnancy and childbirth.

[0365] Example 2

[0366] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0367] The challenge is to provide comprehensive support for first-time pregnant families regarding pregnancy and childbirth while providing personalized advice tailored to each user's emotional state. There is also a need to effectively utilize the information users input daily to provide the necessary support in real time.

[0368] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0369] In this invention, the server includes means for receiving data on the subject's lifestyle patterns, health status, and emotions, means for generating support information related to pregnancy and childbirth based on the data and for analyzing the emotion data to complement and optimize the support information, means for transmitting the support information and displaying it through an application or web portal, means for organizing and storing image data uploaded by users in chronological order, and means for users to input questions and provide appropriate advice using artificial intelligence and emotion analysis technology, thereby enabling users to efficiently receive a variety of support information tailored to their individual situations and emotions.

[0370] "Target individuals" are those who use this system to receive support regarding pregnancy and childbirth.

[0371] "Lifestyle patterns" refers to data such as meal times, sleep times, and daily activity status in the subject's daily life.

[0372] "Health status" refers to data related to the subject's health, such as weight, blood pressure, and whether or not they have morning sickness.

[0373] "Emotion data" refers to data related to emotions obtained from text, voice, biometric sensor data, etc. input by the subject.

[0374] A "server" is a computer system that processes received data and generates and transmits assistance information.

[0375] The "emotion engine" is a technology that analyzes emotional data and uses the results to complement and optimize support information.

[0376] "Support information" includes advice on pregnancy and childbirth, measures to prevent morning sickness, health management suggestions, and procedures for applying for subsidies.

[0377] An "application" is software used by a subject to enter information and receive assistance information.

[0378] A "web portal" is a website where subjects can enter information via a browser and receive support information.

[0379] "Image data" refers to image files uploaded by the subject, such as ultrasound images.

[0380] "Artificial intelligence" refers to technologies such as machine learning and natural language processing that analyze data and provide appropriate advice.

[0381] This invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. In particular, the quality of support is improved by combining it with an emotion engine that recognizes the user's emotions.

[0382] First, the user uses a dedicated application or web portal to input their lifestyle patterns (meal times, sleep times, daily activity status), health status (weight, blood pressure, presence or absence of morning sickness), and emotional data (text input, voice input, biosensor data, etc.). The terminals used here are mobile devices such as smartphones and tablets.

[0383] The device then sends this information to the server, which uses the HTTPS protocol to transfer data securely. The server then stores the received data in a MariaDB database.

[0384] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes information on morning sickness, health management advice, and how to apply for subsidies. Technically, Python scripts are used for data analysis to generate the support information. The emotion engine also uses a generative AI model (e.g., the BERT model) to analyze the user's emotion data. Here, the emotion engine converts the voice data into text and extracts the user's emotion.

[0385] The generated support information is notified to the user by the server via push notification, email, or in-app notification, which is displayed on the user's smartphone or tablet.

[0386] To give a specific example, the user can input the time and contents of breakfast and how "tired" they feel by voice. The device sends this to the server, and the data is saved in a database. Based on the received data, the server generates dietary advice to alleviate morning sickness, reflecting the analysis results of the emotion engine and providing the user with advice such as "We recommend a light breakfast and ensure you get plenty of rest."

[0387] Users can also upload ultrasound images, which the server receives and stores in a database organized by date. If desired, a link can be generated to share the images with family and friends.

[0388] Furthermore, when questions or concerns about pregnancy are entered into the chatbot within the app, the server uses AI and an emotion engine to provide appropriate advice and information. For example, in response to the question, "What should I do if I have severe morning sickness?", the server will respond with, "Make sure to drink plenty of water and eat light meals." If the user is "worried," it will also suggest ways to relax.

[0389] An example of a prompt is as follows:

[0390] "I had bread and eggs at eight o'clock. I'm very tired now."

[0391] "What should I do if I have severe morning sickness?"

[0392] In this way, by utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be provided to families experiencing their first pregnancy.

[0393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0394] Step 1:

[0395] The user launches a dedicated application or web portal and enters data on their lifestyle, health status, and emotions.

[0396] Specific actions: The user opens the app on their smartphone, taps the "Meal Log" button, and then voice-records that they ate bread and eggs for breakfast at 8 a.m. and feel "tired."

[0397] Input: Lifestyle patterns (e.g., meal times, sleep times), health status (e.g., weight, blood pressure), emotional data (text input, voice input)

[0398] Output: The input data is saved on the device.

[0399] Step 2:

[0400] The terminal transmits the input data to the server.

[0401] Specific operation: The terminal converts the input information into packets in real time and sends them securely to the server using the HTTPS protocol.

[0402] Input: User-entered lifestyle patterns, health status, and emotional data

[0403] Output: The data is sent to the server.

[0404] Step 3:

[0405] The server stores the received data in a database.

[0406] Specific operation: The server receives the received information and stores it in the MariaDB database system.

[0407] Input: Data sent from the terminal

[0408] Output: The data is saved to a database.

[0409] Step 4:

[0410] The server analyzes the received data and generates support information, including information on morning sickness, health management advice, and how to apply for subsidies.

[0411] How it works: The server uses a Python script to analyze the received dietary and emotional data and generate dietary advice to alleviate morning sickness. The emotion engine uses the BERT model to convert the audio data into text and extract the emotion "fatigue." Based on this information, it recommends a light breakfast and encourages the user to get some rest.

[0412] Input: User data stored in the database

[0413] Output: Generated support information

[0414] Step 5:

[0415] The server notifies the user of the generated support information.

[0416] Specific operation: The server prepares the generated dietary advice and rest notification and sends it as a push notification. The device receives the notification and displays a message such as "To alleviate morning sickness, we recommend having a light breakfast next time" in the notification bar of the user's smartphone.

[0417] Input: Generated support information

[0418] Output: Sent to the user as a notification.

[0419] Step 6:

[0420] The user receives the notified advice.

[0421] What happens: The user taps on the notification on their smartphone and sees detailed advice in the app.

[0422] Input: Notified support information

[0423] Output: The user views and acts on the advice.

[0424] Step 7:

[0425] Users upload ultrasound photos, which are then stored on a server in chronological order, and if desired, a link is generated to share with family and friends.

[0426] Specific operation: The user taps the "Upload Echo Photo" button, selects and uploads a photo. The server receives the photo, organizes it by date, and stores it in a database. If desired, a sharing link is generated and notified to the user.

[0427] Input: Ultrasound image data

[0428] Output: Organized photo data and a shareable link

[0429] Step 8:

[0430] Users input their questions and concerns into the in-app chatbot, and the server uses AI and an emotion engine to provide appropriate advice.

[0431] Specific operation: When a user asks a question in the app, such as "What should I do if I have severe morning sickness?", they also input their emotions. The server and emotion engine analyze the question and emotion data, and respond with advice such as "Make sure to drink plenty of water and eat light meals." If the user is "worried," the app also suggests further relaxation methods.

[0432] Input: Question content and emotion data

[0433] Output: Good advice

[0434] (Application example 2)

[0435] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0436] Families expecting their first child often need a lot of information about pregnancy and childbirth, and in particular emotional support. However, conventional support systems lack the ability to provide support information that takes into account each individual's emotional state, and provide intuitive information acquisition methods using virtual environments, which means they are unable to fully alleviate users' anxiety and stress.

[0437] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data on the subject's lifestyle patterns and health condition, means for generating support information related to pregnancy and childbirth based on the data, means for analyzing emotion data and complementing and optimizing the support information, means for displaying the generated support information in a virtual environment, and means for transmitting the support information. This makes it possible for the user to intuitively obtain personalized support information in a virtual environment that takes into account the user's emotional state, thereby reducing anxiety and stress.

[0438] "Target" refers to users who use this system to receive support regarding pregnancy and childbirth.

[0439] "Lifestyle patterns" refers to the subject's daily habits and behaviors, specifically including meal times, sleep times, daily activity status, etc.

[0440] "Health condition" is information that indicates the physical condition of the subject, and specifically includes weight, blood pressure, whether or not the subject has morning sickness, and the like.

[0441] "Emotion data" is information that indicates the emotional state of a subject, and specifically includes text input, voice input, biometric sensor data, and the like.

[0442] "Support information" refers to advice and guidance regarding pregnancy and childbirth that is generated based on the subject's lifestyle patterns and health status.

[0443] An "emotion recognition engine" is software that analyzes input emotional data and recognizes the emotional state of the subject.

[0444] A "virtual environment" is a virtual reality space that subjects can experience using a head-mounted display or smartphone.

[0445] A "virtual display" is a means of displaying information in a virtual reality space, allowing the subject to intuitively obtain the information.

[0446] "Complement and optimization" refers to using the analyzed emotional data to adjust the support information to the emotional state of the target person and provide it in the most optimal form.

[0447] A "generative AI model" is a model that generates information using artificial intelligence (AI), specifically performing natural language processing and data analysis.

[0448] This invention is a system that allows a subject to receive comprehensive support regarding pregnancy and childbirth, and improves the quality of support by combining an emotion engine. The following describes in detail the mode for carrying out the invention.

[0449] 1. Data Entry and Emotion Recognition

[0450] Users use a smartphone or head-mounted display to input data on their daily life patterns (e.g., meal times, sleep time, activity status) and health status (e.g., weight, blood pressure, presence or absence of morning sickness). Emotional data can also be input using text input, voice input, biosensor data, etc. The device then transmits this data to the server in real time.

[0451] A specific example of its use is when a user enters the time and content of breakfast into the application's food diary, and then reports by voice that they feel "tired" using the emotion engine. This data is sent from the device to the server and stored in a database.

[0452] 2. Data analysis and generation of supporting information

[0453] The server generates support information for pregnancy and childbirth based on the received data. The support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[0454] 3. Sending support information and displaying it in the virtual environment

[0455] The support information generated by the server is displayed intuitively to the user in the virtual environment. The user can efficiently receive the support information via a head-mounted display or smartphone. This information can also be sent via push notifications or email.

[0456] Specific examples

[0457] 1. The user inputs the time and content of meals they had eaten that day, as well as the emotions they felt. Based on this information, the server and emotion engine recommend a lighter meal for the next meal, and if the user feels "tired," it also suggests taking time to rest.

[0458] 2. The user enters their address, number of weeks pregnant, and their feelings. The server and emotion engine search for local subsidy information and guides them on how to apply for the appropriate subsidy. If the user feels anxious, they will also be provided with information on psychological support.

[0459] Prompt Sentence Examples

[0460] "When a user asks 'What to do if morning sickness is severe,' we know that the user is feeling 'worried.' Please provide appropriate advice and suggest ways to relax."

[0461] This allows support information to be generated that takes into account the user's emotional state, enabling intuitive information acquisition in a virtual environment.

[0462] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0463] Step 1:

[0464] The user uses a smartphone or head-mounted display to input lifestyle patterns (e.g., meal times, sleep times, activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biosensor data). This data is sent to the server in real time via the application.

[0465] Input: Life patterns, health status, emotional data

[0466] Output: User data sent to the server

[0467] Specific operation: The user enters various data within the app and presses the send button.

[0468] Step 2:

[0469] The server stores the received data in a database and prepares it for analysis. The stored data is categorized into lifestyle patterns, health status, and emotional data.

[0470] Input: Various data sent by the user

[0471] Output: Classified user data

[0472] Specific behavior: Inserts received data into a database and organizes it by category.

[0473] Step 3:

[0474] The server analyzes the stored data and generates support information for users regarding pregnancy and childbirth, including information on how to deal with morning sickness, health management advice, and how to apply for subsidies.

[0475] Input: Classified user data

[0476] Output: Generated support information

[0477] Specific operation: Executes the analysis algorithm and generates appropriate support information.

[0478] Step 4:

[0479] The server uses an emotion engine to analyze the user's emotional data and complements and optimizes the support information based on the results. For example, if the user feels "fatigue," it will suggest taking time to rest.

[0480] Input: User emotion data and assistance information generated in the previous step

[0481] Output: Optimized support information

[0482] Specific operation: Emotion data is input into the emotion engine and reflected in the support information.

[0483] Step 5:

[0484] The server sends the generated and optimized assistance information to a head-mounted display or smartphone for display in the virtual environment, and also notifies the user of the information via push notification or email as needed.

[0485] Input: Optimized support information

[0486] Output: Assistance information displayed on the user's device

[0487] Specific behavior: Sends assistance information to the appropriate device and converts it into a format for display.

[0488] Step 6:

[0489] Users can view support information in a virtual environment and use it to manage their health and take various steps related to pregnancy, such as checking dietary tips to combat morning sickness or specific procedures for applying for subsidies.

[0490] Input: Assistive information displayed on the device

[0491] Output: User actions and decisions

[0492] Specific actions: Plan and execute actions based on support information.

[0493] The above processing steps provide personalized support information that takes into account the user's emotional state, and enable intuitive acquisition in a virtual environment.

[0494] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0495] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0496] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0497] [Second embodiment]

[0498] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0499] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0500] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0501] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0502] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0503] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0504] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0505] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0506] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0507] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0508] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0509] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0510] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments of the system are described below.

[0511] 1. Entering information about the subject and receiving data

[0512] The user uses a dedicated application or web portal to enter data on their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and the device then sends this information to the server.

[0513] Examples:

[0514] 1. The user enters the time and type of breakfast they had into the food diary within the application.

[0515] 2. The terminal sends the entered data to the server and stores it in the database.

[0516] 2. Data analysis and generation of supporting information

[0517] Based on the data received by the server, support information related to pregnancy and childbirth is generated, including information on morning sickness, health management advice, and how to apply for subsidies.

[0518] Examples:

[0519] 1. The server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness.

[0520] 2. The server advises the user on the appropriate pace of weight gain based on the weight data entered by the user.

[0521] 3. Sending and displaying support information

[0522] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[0523] Examples:

[0524] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification.

[0525] 2. The user opens the application and views the provided advice.

[0526] Details of each function of the system

[0527] Morning sickness prevention advice

[0528] The server analyzes the user's lifestyle patterns and provides advice on diet and lifestyle changes to alleviate morning sickness, such as adjusting the timing and content of meals and suggesting relaxation methods.

[0529] Examples:

[0530] 1. The user enters the meal times and contents for the day.

[0531] 2. The server uses that information to send you a notification recommending a lighter meal at your next meal.

[0532] Subsidy application support

[0533] The server provides information about available grants based on the user's situation and guides them through the application process, providing a checklist of required documents and deadlines for submission.

[0534] Examples:

[0535] 1. The user enters their address and number of weeks pregnant.

[0536] 2. The server searches for local grant information and sends notifications guiding users on how to apply for relevant grants.

[0537] health care

[0538] The server analyzes weight and blood pressure data entered periodically, and if any of the data deviates from the normal range, it will issue a warning to the user and recommend that they visit an appropriate medical institution.

[0539] Examples:

[0540] 1. The user enters their daily weight and blood pressure.

[0541] 2. The server detects an abnormal value and notifies the user to seek medical attention immediately.

[0542] Storage and sharing of ultrasound images

[0543] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[0544] Examples:

[0545] 1. The user uploads their most recent ultrasound photo to the app.

[0546] 2. The server organizes the photos and generates links for family members to share them.

[0547] Communication Features

[0548] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI to provide appropriate advice and information, creating a situation where users can receive support at any time.

[0549] Examples:

[0550] 1. A user asks within the app, "What should I do if I have severe morning sickness?"

[0551] 2. The server uses an AI engine to analyze the question and respond with advice such as, "Make sure to drink plenty of water and eat light meals."

[0552] The above is an embodiment of the present invention. This system makes it possible to consistently provide a wide range of support to families expecting their first pregnancy.

[0553] The processing flow will be explained below.

[0554] 1. Server-based support for the grant application process

[0555] Step 1:

[0556] The user enters their address and number of weeks pregnant into a dedicated form, and the device sends the entered information to the server.

[0557] Step 2:

[0558] The server receives the submitted information and searches the database for a list of grants based on the region and gestational age.

[0559] Step 3:

[0560] The server obtains detailed information about the subsidy (eligibility criteria, deadline, required documents, etc.) and notifies the user.

[0561] Step 4:

[0562] The user scans and uploads the necessary documents, and the device sends the uploaded documents to the server.

[0563] Step 5:

[0564] The server uses OCR technology to read the document and check the requirements. If there are any errors, the server notifies the user and requests them to make corrections.

[0565] Step 6:

[0566] After verifying that all required documents are received, the server initiates the application process, submits the application to the designated government agency, and notifies the user of successful submission.

[0567] 2. Advice for dealing with morning sickness

[0568] Step 1:

[0569] The user inputs their lifestyle patterns (meal times, stress levels, daily activities), and the device sends the input information to the server.

[0570] Step 2:

[0571] Based on the information sent, the server searches the database for individually appropriate morning sickness prevention advice and optimizes it using an algorithm.

[0572] Step 3:

[0573] The server generates the advice and notifies the user.

[0574] Step 4:

[0575] The user puts the provided advice into practice and provides feedback on the results (whether it was effective or not). The device then sends the feedback information to the server.

[0576] Step 5:

[0577] The server analyzes the feedback information and updates and adjusts the advice as needed. The server notifies the user of new advice on a weekly basis.

[0578] 3. Support for balancing work and family life

[0579] Step 1:

[0580] The user enters information about their work duties, working hours, and the size of the company. The device then sends the input data to the server.

[0581] Step 2:

[0582] Based on the information entered, the server searches a database for information on industry-specific countermeasures and legal regulations.

[0583] Step 3:

[0584] The server provides users with information on maternity and childcare leave systems and workplace solutions.

[0585] Step 4:

[0586] If a user needs tips on communication in the workplace or advice on time management, the user inputs this information. The terminal then sends the input data to the server.

[0587] Step 5:

[0588] The server generates appropriate advice and notifies the user.

[0589] 4. Gathering information on delivery hospitals and support in selecting them

[0590] Step 1:

[0591] The user inputs the conditions of the hospital they want to visit (whether they offer painless childbirth, the facilities, etc.). The terminal sends the input data to the server.

[0592] Step 2:

[0593] The server searches a database of relevant hospitals in the area based on the entered criteria.

[0594] Step 3:

[0595] The server generates a list of candidate hospitals and provides detailed information to the user.

[0596] Step 4:

[0597] The user browses the detailed information of the candidate hospitals and makes a selection. The terminal sends the selection information to the server.

[0598] Step 5:

[0599] The server saves the selected hospital in the user's favorites list and notifies them of the latest information.

[0600] 5. Health management data entry and abnormal value detection

[0601] Step 1:

[0602] The user periodically inputs weight and blood pressure measurement data into the app, and the device sends the input data to the server.

[0603] Step 2:

[0604] The server compares and analyzes the received data with past data.

[0605] Step 3:

[0606] If the server detects an abnormal value, it generates a warning and notifies the user.

[0607] Step 4:

[0608] The user receives a notification of an abnormal value and inputs a request for information on how to respond. The device sends the input data to the server.

[0609] Step 5:

[0610] The server searches for nearby medical institutions and provides a list of appropriate medical institutions.

[0611] 6. Digital storage and sharing of ultrasound images

[0612] Step 1:

[0613] The user scans the Echo photo and uploads it using the device, which then sends the uploaded photo to the server.

[0614] Step 2:

[0615] The server organizes the photos by date and displays them in an album format.

[0616] Step 3:

[0617] The user selects who (family, friends) they want to share the photos with, and the device sends the selection information to the server.

[0618] Step 4:

[0619] The server generates a shared link and notifies the specified recipient.

[0620] 7. Consultation function as a communication partner

[0621] Step 1:

[0622] The user inputs a question into the chatbot within the app, and the device sends the input data to the server.

[0623] Step 2:

[0624] The server uses an AI engine to analyze the question and find the appropriate answer.

[0625] Step 3:

[0626] The server notifies the user of the generated answer.

[0627] Step 4:

[0628] The user enters an additional question, and the device sends the input data to the server again.

[0629] Step 5:

[0630] The server reparses and generates successively appropriate answers.

[0631] Example 1

[0632] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] Families expecting their first pregnancy need information and support regarding pregnancy and childbirth, but there are limited ways to receive appropriate advice and support. In particular, important information such as measures to combat morning sickness and how to apply for subsidies needs to be tailored to each individual's situation. For this reason, a system that can provide efficient and comprehensive support is needed.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0635] In this invention, the server includes a means for inputting data on the lifestyle patterns and health conditions of the subject, a means for transmitting the data from a terminal to the server, a server equipped with a data analysis means for generating support information related to pregnancy and childbirth based on the data, and a means for transmitting the support information from the server to the terminal and notifying the user. This makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[0636] "Subject's lifestyle patterns" refers to the totality of an individual's daily behaviors and habits, including meal times, sleep times, and daily activity status.

[0637] "Health status" refers to data that indicates an individual's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[0638] "Means for inputting data" refers to the means by which users input information about their lifestyle patterns and health status, such as using a dedicated application or web portal.

[0639] "Means for transmitting data from a terminal to a server" refers to a communication means for transmitting data from a terminal (e.g., smartphone, PC) to a server via the Internet.

[0640] "Data analysis means" refers to algorithms and software that analyze and process data in order to generate support information related to pregnancy and childbirth based on the received data.

[0641] "Support information" refers to a comprehensive range of information related to pregnancy and childbirth, such as measures to prevent morning sickness, health management advice, and how to apply for subsidies.

[0642] "Server" refers to a computer system for storing data, analyzing data, generating and transmitting support information.

[0643] "Terminal" refers to a device used by a user to input and receive data, such as a smartphone or computer.

[0644] "Means of notification" refers to methods such as push notification, email, or in-app notification for notifying the user of the generated support information.

[0645] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments are described below.

[0646] First, users access a dedicated application or web portal and enter data about their lifestyle and health conditions, including meal times, sleep time, daily activity levels, weight, blood pressure, and whether they experience morning sickness. This data is entered from the user's smartphone, computer, or other device.

[0647] Next, the terminal sends the input data to a server via the Internet. At this time, the data is sent safely using a secure communication method. The server receives the data and stores it in a database.

[0648] The server analyzes the received data using a generative AI model (e.g., GPT-3 or BERT) to generate personalized support information based on the user's lifestyle and health status. This support information covers a wide range of topics, including advice on morning sickness, health management, and how to apply for subsidies.

[0649] As a specific example, if a user inputs the time and content of their breakfast, such as "I had toast and orange juice at 7:30," the server will analyze that information and generate specific dietary advice to alleviate morning sickness, such as "I recommend having some easily digestible fruit and light protein for your next breakfast."

[0650] The generated support information is sent from the server to the user's device. Notifications can be sent in various ways, such as push notifications, emails, and in-app notifications. For example, advice on how to deal with morning sickness can be sent as a push notification, and when the user taps the notification to open the application, detailed advice will be displayed.

[0651] An example of a prompt sentence would be, "What should I do if I have severe morning sickness?" In this case, the server will analyze it using an AI engine and respond with specific advice such as, "Try to drink plenty of fluids and eat light meals."

[0652] This system makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[0653] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0654] Step 1:

[0655] Users access a dedicated application or web portal and enter their lifestyle patterns and health status. Data entered includes meal times, meal contents, sleep time, daily activity status, weight, blood pressure, and whether or not they have morning sickness. For example, users enter information such as "I had toast and orange juice for breakfast at 7:30." This information is saved on the device as initial data.

[0656] Step 2:

[0657] The device sends the entered data to the server. The data is sent via the Internet using a secure communication method. Specifically, the data sent includes the meal details and time entered by the user, and the information "I had toast and orange juice at 7:30" is transferred. The server receives this data and stores it in a database.

[0658] Step 3:

[0659] The server analyzes the received data. Based on the received data, a generative AI model (e.g., GPT-3 or BERT) is used to understand the user's lifestyle patterns and health status. Here, data from the past few weeks is statistically analyzed to identify trends such as "irregular breakfast times." This allows for a deeper understanding of the user's lifestyle patterns.

[0660] Step 4:

[0661] The server generates support information for pregnancy and childbirth based on the analysis results. Based on the lifestyle patterns and health condition data obtained from the analysis, the generative AI model generates appropriate advice and support information. For example, the generated advice might be, "We recommend that you eat easily digestible fruit and light protein for your next breakfast." The generated support information is temporarily stored on the server.

[0662] Step 5:

[0663] The server sends the generated support information to the user's device. Notification methods can be selected from a variety of methods, including push notifications, emails, and in-app notifications. A specific example of how this works is a notification such as, "To alleviate morning sickness, we recommend eating easily digestible fruit and light protein for breakfast."

[0664] Step 6:

[0665] The user receives a notification on their device and opens the app or web portal to view the support information. When the user taps the notification on their smartphone, detailed advice is displayed within the app, allowing the user to review the support information provided and incorporate it into their daily life.

[0666] Through the above processing steps, efficient and timely support information is provided to families expecting their first pregnancy.

[0667] (Application example 1)

[0668] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0669] Families expecting their first pregnancy need comprehensive support regarding pregnancy and childbirth, but current systems lack information tailored to each individual user. Furthermore, there is no mechanism in place to provide the necessary content in a timely manner, making it difficult for information recipients to receive the necessary support at the appropriate time. This creates a problem in which users are unable to feel at ease during their pregnancy.

[0670] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0671] In this invention, the server includes a means for receiving data on the lifestyle patterns and health conditions of a subject, a means for generating support information related to pregnancy and childbirth based on the data, a means for generating the support information as content suitable for each individual, and a means for delivering the content to the user, thereby enabling personalized content optimized for each individual user to be delivered in a timely manner.

[0672] "Covered Person" refers to an individual or family member who needs assistance with pregnancy and childbirth.

[0673] "Lifestyle patterns" refers to the subject's daily habits and behaviors, such as meal times, sleep times, and activity levels.

[0674] "Health status" refers to the subject's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[0675] "Data receiving means" refers to a device or method for acquiring data on the subject's lifestyle patterns and health status and inputting it into the system.

[0676] "Support information generation means" refers to a device or method that generates appropriate advice and information regarding pregnancy and childbirth based on the received data.

[0677] The "content generation means" refers to a device or method for creating the generated support information as content in a form optimized for each individual target person.

[0678] "Content distribution means" refers to a device or method for distributing generated content to a target person in a timely manner.

[0679] "Morning sickness advice" refers to information that includes specific suggestions for dietary and lifestyle changes to reduce morning sickness symptoms.

[0680] "Subsidy application support information" refers to details of subsidies available to eligible persons and support information regarding the application procedures.

[0681] MODE FOR CARRYING OUT THE INVENTION

[0682] The present invention provides a system for providing comprehensive support for pregnancy and childbirth to a subject. Specific embodiments of the system are described below.

[0683] The system has the following main functions:

[0684] 1. Entering information about the subject and receiving data

[0685] Users enter data about their lifestyle and health status via a dedicated application or web portal, including meal times, sleep time, activity level, weight, blood pressure, and whether they suffer from morning sickness. The data entered by the user is sent to a server via the device, where it is stored in the system's central database.

[0686] As a specific example, when a user enters the time and content of breakfast into an application, the device sends the information to a server and stores it in a database.

[0687] 2. Data analysis and generation of supporting information

[0688] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes advice on how to deal with morning sickness, health management, and assistance with applying for subsidies. Furthermore, the support information is generated as personalized content and provided in the most appropriate format for each individual user.

[0689] As a specific example, the server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness. It also advises the user on the appropriate pace of weight gain based on their weight data.

[0690] 3. Sending and displaying support information

[0691] The server notifies the user of the generated assistance information and displays it through the application or web portal. Notifications can be made via push notifications, emails, or in-application notifications. The user can then use their device to review the received assistance information and adjust their actions accordingly.

[0692] For example, the server generates morning sickness advice and sends it to the user's smartphone as a push notification. When the user opens the application, they can view the advice.

[0693] Hardware and software used

[0694] Smartphone: Used as a device for entering information and receiving notifications.

[0695] Server: Used as a backend server for data analysis, support information generation, and notification distribution. For example, AWS or Google Cloud Platform can be used.

[0696] AI engines: Used for data analysis and content generation, specifically generative AI models such as TensorFlow and PyTorch.

[0697] Examples of specific examples and prompts

[0698] Below are examples of specific prompt sentences to be fed into the generative AI model.

[0699] Analyze the user's lifestyle and health data to generate recommended content for the day, such as dietary advice to alleviate morning sickness or exercise advice for weight management.

[0700] By using such prompts, the AI ​​engine can generate appropriate support information and provide it to the user in a personalized format, enabling the system to provide the necessary information at the appropriate time during pregnancy.

[0701] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0702] Step 1:

[0703] A user uses a dedicated application or web portal to input data about their lifestyle and health status (e.g., meal times, sleep time, activity level, weight, blood pressure, presence or absence of morning sickness). The input data is received by the device. The input in this step is data about the user's lifestyle and health status, and the output is data stored in the device.

[0704] Step 2:

[0705] The terminal sends the data entered by the user to the server. Specifically, the terminal sends the data to the specified endpoint via the network. The input is the data stored on the terminal, and the output is the data received by the server.

[0706] Step 3:

[0707] The server analyzes the data it receives. Specifically, the server uses an AI engine to generate support information for pregnancy and childbirth based on the user's lifestyle patterns and health status data. At this time, personalized content is generated by a generative AI model. The input is the data received by the server, and the output is the generated support information.

[0708] Step 4:

[0709] Based on the support information generated by the server, the server generates optimal content for each individual user. Specifically, content such as advice on how to deal with morning sickness and health management is created based on the results obtained from the AI ​​engine. The input is analyzed data, and the output is personalized content.

[0710] Step 5:

[0711] The server notifies the user of the generated content. Specifically, the server sends a push notification, email, or in-app notification to the device. The input is the generated content, and the output is the notification to the user.

[0712] Step 6:

[0713] The user receives a notification and opens an application or web portal to view supporting information. Specifically, the user reviews the content within the app and adjusts their behavior accordingly. The input is the received notification and the output is the user action.

[0714] In this way, the entire system works together to provide users with comprehensive and personalized support regarding pregnancy and childbirth.

[0715] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0716] This invention is a system that provides comprehensive support regarding pregnancy and childbirth to families expecting their first pregnancy, and by combining it with an emotion engine that recognizes the user's emotions, it can provide even more comprehensive support.

[0717] 1. Entering information about the subject and receiving data

[0718] The user uses a dedicated application or web portal to input their lifestyle patterns (e.g., meal times, sleep times, daily activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biometric sensor data, etc.). The device then sends this information to the server.

[0719] Examples:

[0720] 1. The user enters the time and contents of breakfast into the food diary within the application, and the emotion engine reports via voice input that they feel "tired."

[0721] 2. The device sends the input data and voice data to the server and stores them in a database.

[0722] 2. Data analysis and generation of supporting information

[0723] Based on the data received by the server, it generates support information related to pregnancy and childbirth. Support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[0724] Examples:

[0725] 1. The server analyzes the user's eating patterns and emotional data and generates dietary advice to alleviate morning sickness. If the user feels "fatigue," the server generates advice that takes this into account.

[0726] 2. The server advises the user on the appropriate pace of weight gain based on the weight and emotional data entered by the user. If the user feels "anxious," it also suggests ways to relax.

[0727] 3. Sending and displaying support information

[0728] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[0729] Examples:

[0730] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification. If the user feels "fatigue," it also sends a notification encouraging them to take a rest.

[0731] 2. The user opens the application and views the provided advice.

[0732] More morning sickness advice

[0733] The server and emotion engine work together to analyze the user's lifestyle patterns and emotional data, and provide advice on dietary and lifestyle changes to alleviate morning sickness.

[0734] Examples:

[0735] 1. The user inputs the times and contents of meals they ate each day, as well as the emotions they felt.

[0736] 2. Based on this information, the server and emotion engine will recommend a lighter meal for the next meal and, if the user feels "tired," will also suggest taking time to rest.

[0737] Subsidy application support details

[0738] The server and emotion engine work together to provide applicable grant information and guide the application process based on the user's situation and emotion data. It also provides a checklist of required documents and deadlines for submission. If the user feels anxious or stressed, it also provides psychological support information.

[0739] Examples:

[0740] 1. The user enters their address, how many weeks pregnant they are, and how they feel.

[0741] 2. The server and emotion engine search for local subsidy information and guide users on how to apply for the appropriate subsidy, while also providing psychological support information if the user feels "anxious."

[0742] Health Management Details

[0743] The server analyzes weight and blood pressure data entered periodically and issues a warning if the data falls outside the normal range. Furthermore, the emotion engine analyzes the emotional data and suggests appropriate relaxation methods or visiting a medical institution if the user is feeling anxious or stressed.

[0744] Examples:

[0745] 1. The user enters their daily weight, blood pressure, and emotional data.

[0746] 2. The server and emotion engine detect abnormal values, notify the user to seek medical attention immediately, and suggest relaxation methods.

[0747] Learn more about storing and sharing ultrasound photos

[0748] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[0749] Examples:

[0750] 1. The user uploads their most recent ultrasound photo to the app.

[0751] 2. The server organizes the photos and generates links for family members to share them.

[0752] Details of the consultation function as a communication partner

[0753] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI and an emotion engine to provide appropriate advice and information, thereby providing even greater support.

[0754] Examples:

[0755] 1. When users ask questions in the app such as "What should I do if I have severe morning sickness?", they also input the emotions they are feeling.

[0756] 2. The server and emotion engine analyze the question and respond with advice such as "Make sure to drink plenty of water and eat light meals," and if the user is "worried," it also suggests further ways to relax.

[0757] The above is a detailed embodiment for carrying out the present invention. By utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be realized for families experiencing their first pregnancy.

[0758] The processing flow will be explained below.

[0759] Processing flow of a system that combines emotion engines

[0760] 1. Information input and data reception

[0761] Step 1:

[0762] Users enter their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness) into a dedicated application or web portal, along with emotional data (e.g., text input, voice input, biometric sensor data, etc.).

[0763] Step 2:

[0764] The terminal transmits the information entered by the user to the server.

[0765] Step 3:

[0766] The server stores the received information in a database, and sends the emotion data to the emotion engine.

[0767] 2. Data analysis and generation of supporting information

[0768] Step 1:

[0769] The server analyzes the stored lifestyle patterns, health status, and emotional data.

[0770] Step 2:

[0771] The emotion engine analyzes the emotion data and identifies the user's current emotional state (e.g., "tired," "anxious," "stressed").

[0772] Step 3:

[0773] The server generates support information for pregnancy and childbirth based on the analysis results, and also reflects the analysis results of the emotion engine.

[0774] 3. Sending and displaying support information

[0775] Step 1:

[0776] The server notifies the user of the assistance information generated by the server via push notification, email, or in-app notification.

[0777] Step 2:

[0778] The user opens the application and views the notified support information.

[0779] Specific function processing flow

[0780] Morning sickness prevention advice

[0781] Step 1:

[0782] Users enter the time and content of their meals throughout the day, as well as the emotions they are feeling, into the app.

[0783] Step 2:

[0784] The device sends input data and emotion data to the server.

[0785] Step 3:

[0786] The server analyzes the data and generates dietary advice to alleviate morning sickness.

[0787] Step 4:

[0788] The emotion engine analyzes the emotional data and, if the user is feeling "tired," generates advice that takes that state into account.

[0789] Step 5:

[0790] The server notifies the user of the generated advice.

[0791] Subsidy application support

[0792] Step 1:

[0793] The user enters their address, how many weeks pregnant they are, and how they are feeling.

[0794] Step 2:

[0795] The terminal transmits the input data and emotion data to the server.

[0796] Step 3:

[0797] The server searches for local grant information and generates notifications guiding users on how to apply for the appropriate grant.

[0798] Step 4:

[0799] The emotion engine analyzes the user's emotional data, and if the user is feeling "anxiety," it also generates mental support information that takes that state into account.

[0800] Step 5:

[0801] The server generates grant application guides and provides moral support information to the user.

[0802] Health management data analysis and outlier detection

[0803] Step 1:

[0804] Users periodically enter their weight, blood pressure, and emotional data into the app.

[0805] Step 2:

[0806] The terminal sends the input data to the server.

[0807] Step 3:

[0808] The server analyzes the data and generates alerts if outliers are detected.

[0809] Step 4:

[0810] The emotion engine analyzes the emotional data and, if the user is feeling "worried" or "stressed," suggests relaxation methods or visiting a medical institution that take that state into consideration.

[0811] Step 5:

[0812] The server will warn the user about the abnormal value and inform them how to relax.

[0813] Storage and sharing of ultrasound images

[0814] Step 1:

[0815] The user scans the ultrasound photo and uploads it using the device.

[0816] Step 2:

[0817] The device sends the uploaded photos to the server.

[0818] Step 3:

[0819] The server organizes the photos by date and displays them in an album format.

[0820] Step 4:

[0821] The user selects who (family, friends) they want to share the photos with.

[0822] Step 5:

[0823] The terminal transmits the selection information to the server.

[0824] Step 6:

[0825] The server generates a shared link and notifies the specified recipient.

[0826] Consultation function as a communication partner

[0827] Step 1:

[0828] Users input questions into the in-app chatbot, along with their emotional state.

[0829] Step 2:

[0830] The terminal sends the input data to the server.

[0831] Step 3:

[0832] The server uses an AI engine and an emotion engine to analyze the question and emotion data and search for the appropriate answer.

[0833] Step 4:

[0834] The server notifies the user of the generated answer, along with additional advice based on the user's emotional state.

[0835] Step 5:

[0836] The user enters a follow-up question.

[0837] Step 6:

[0838] The terminal again sends the input data to the server.

[0839] Step 7:

[0840] The server re-parses and subsequently generates the appropriate answer and notifies the user.

[0841] The above is a specific implementation of a system that combines an emotion engine. This processing flow allows users to receive more personalized support for pregnancy and childbirth.

[0842] Example 2

[0843] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0844] The challenge is to provide comprehensive support for first-time pregnant families regarding pregnancy and childbirth while providing personalized advice tailored to each user's emotional state. There is also a need to effectively utilize the information users input daily to provide the necessary support in real time.

[0845] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0846] In this invention, the server includes means for receiving data on the subject's lifestyle patterns, health status, and emotions, means for generating support information related to pregnancy and childbirth based on the data and for analyzing the emotion data to complement and optimize the support information, means for transmitting the support information and displaying it through an application or web portal, means for organizing and storing image data uploaded by users in chronological order, and means for users to input questions and provide appropriate advice using artificial intelligence and emotion analysis technology, thereby enabling users to efficiently receive a variety of support information tailored to their individual situations and emotions.

[0847] "Target individuals" are those who use this system to receive support regarding pregnancy and childbirth.

[0848] "Lifestyle patterns" refers to data such as meal times, sleep times, and daily activity status in the subject's daily life.

[0849] "Health status" refers to data related to the subject's health, such as weight, blood pressure, and whether or not they have morning sickness.

[0850] "Emotion data" refers to data related to emotions obtained from text, voice, biometric sensor data, etc. input by the subject.

[0851] A "server" is a computer system that processes received data and generates and transmits assistance information.

[0852] The "emotion engine" is a technology that analyzes emotional data and uses the results to complement and optimize support information.

[0853] "Support information" includes advice on pregnancy and childbirth, measures to prevent morning sickness, health management suggestions, and procedures for applying for subsidies.

[0854] An "application" is software used by a subject to enter information and receive assistance information.

[0855] A "web portal" is a website where subjects can enter information via a browser and receive support information.

[0856] "Image data" refers to image files uploaded by the subject, such as ultrasound images.

[0857] "Artificial intelligence" refers to technologies such as machine learning and natural language processing that analyze data and provide appropriate advice.

[0858] This invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. In particular, the quality of support is improved by combining it with an emotion engine that recognizes the user's emotions.

[0859] First, the user uses a dedicated application or web portal to input their lifestyle patterns (meal times, sleep times, daily activity status), health status (weight, blood pressure, presence or absence of morning sickness), and emotional data (text input, voice input, biosensor data, etc.). The terminals used here are mobile devices such as smartphones and tablets.

[0860] The device then sends this information to the server, which uses the HTTPS protocol to transfer data securely. The server then stores the received data in a MariaDB database.

[0861] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes information on morning sickness, health management advice, and how to apply for subsidies. Technically, Python scripts are used for data analysis to generate the support information. The emotion engine also uses a generative AI model (e.g., the BERT model) to analyze the user's emotion data. Here, the emotion engine converts the voice data into text and extracts the user's emotion.

[0862] The generated support information is notified to the user by the server via push notification, email, or in-app notification, which is displayed on the user's smartphone or tablet.

[0863] To give a specific example, the user can input the time and contents of breakfast and how "tired" they feel by voice. The device sends this to the server, and the data is saved in a database. Based on the received data, the server generates dietary advice to alleviate morning sickness, reflecting the analysis results of the emotion engine and providing the user with advice such as "We recommend a light breakfast and ensure you get plenty of rest."

[0864] Users can also upload ultrasound images, which the server receives and stores in a database organized by date. If desired, a link can be generated to share the images with family and friends.

[0865] Furthermore, when questions or concerns about pregnancy are entered into the chatbot within the app, the server uses AI and an emotion engine to provide appropriate advice and information. For example, in response to the question, "What should I do if I have severe morning sickness?", the server will respond with, "Make sure to drink plenty of water and eat light meals." If the user is "worried," it will also suggest ways to relax.

[0866] An example of a prompt is as follows:

[0867] "I had bread and eggs at eight o'clock. I'm very tired now."

[0868] "What should I do if I have severe morning sickness?"

[0869] In this way, by utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be provided to families experiencing their first pregnancy.

[0870] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0871] Step 1:

[0872] The user launches a dedicated application or web portal and enters data on their lifestyle, health status, and emotions.

[0873] Specific actions: The user opens the app on their smartphone, taps the "Meal Log" button, and then voice-records that they ate bread and eggs for breakfast at 8 a.m. and feel "tired."

[0874] Input: Lifestyle patterns (e.g., meal times, sleep times), health status (e.g., weight, blood pressure), emotional data (text input, voice input)

[0875] Output: The input data is saved on the device.

[0876] Step 2:

[0877] The terminal transmits the input data to the server.

[0878] Specific operation: The terminal converts the input information into packets in real time and sends them securely to the server using the HTTPS protocol.

[0879] Input: User-entered lifestyle patterns, health status, and emotional data

[0880] Output: The data is sent to the server.

[0881] Step 3:

[0882] The server stores the received data in a database.

[0883] Specific operation: The server receives the received information and stores it in the MariaDB database system.

[0884] Input: Data sent from the terminal

[0885] Output: The data is saved to a database.

[0886] Step 4:

[0887] The server analyzes the received data and generates support information, including information on morning sickness, health management advice, and how to apply for subsidies.

[0888] How it works: The server uses a Python script to analyze the received dietary and emotional data and generate dietary advice to alleviate morning sickness. The emotion engine uses the BERT model to convert the audio data into text and extract the emotion "fatigue." Based on this information, it recommends a light breakfast and encourages the user to get some rest.

[0889] Input: User data stored in the database

[0890] Output: Generated support information

[0891] Step 5:

[0892] The server notifies the user of the generated support information.

[0893] Specific operation: The server prepares the generated dietary advice and rest notification and sends it as a push notification. The device receives the notification and displays a message such as "To alleviate morning sickness, we recommend having a light breakfast next time" in the notification bar of the user's smartphone.

[0894] Input: Generated support information

[0895] Output: Sent to the user as a notification.

[0896] Step 6:

[0897] The user receives the notified advice.

[0898] What happens: The user taps on the notification on their smartphone and sees detailed advice in the app.

[0899] Input: Notified support information

[0900] Output: The user views and acts on the advice.

[0901] Step 7:

[0902] Users upload ultrasound photos, which are then stored on a server in chronological order, and if desired, a link is generated to share with family and friends.

[0903] Specific operation: The user taps the "Upload Echo Photo" button, selects and uploads a photo. The server receives the photo, organizes it by date, and stores it in a database. If desired, a sharing link is generated and notified to the user.

[0904] Input: Ultrasound image data

[0905] Output: Organized photo data and a shareable link

[0906] Step 8:

[0907] Users input their questions and concerns into the in-app chatbot, and the server uses AI and an emotion engine to provide appropriate advice.

[0908] Specific operation: When a user asks a question in the app, such as "What should I do if I have severe morning sickness?", they also input their emotions. The server and emotion engine analyze the question and emotion data, and respond with advice such as "Make sure to drink plenty of water and eat light meals." If the user is "worried," the app also suggests further relaxation methods.

[0909] Input: Question content and emotion data

[0910] Output: Good advice

[0911] (Application example 2)

[0912] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0913] Families expecting their first child often need a lot of information about pregnancy and childbirth, and in particular emotional support. However, conventional support systems lack the ability to provide support information that takes into account each individual's emotional state, and provide intuitive information acquisition methods using virtual environments, which means they are unable to fully alleviate users' anxiety and stress.

[0914] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data on the subject's lifestyle patterns and health condition, means for generating support information related to pregnancy and childbirth based on the data, means for analyzing emotion data and complementing and optimizing the support information, means for displaying the generated support information in a virtual environment, and means for transmitting the support information. This makes it possible for the user to intuitively obtain personalized support information in a virtual environment that takes into account the user's emotional state, thereby reducing anxiety and stress.

[0915] "Target" refers to users who use this system to receive support regarding pregnancy and childbirth.

[0916] "Lifestyle patterns" refers to the subject's daily habits and behaviors, specifically including meal times, sleep times, daily activity status, etc.

[0917] "Health condition" is information that indicates the physical condition of the subject, and specifically includes weight, blood pressure, whether or not the subject has morning sickness, and the like.

[0918] "Emotion data" is information that indicates the emotional state of a subject, and specifically includes text input, voice input, biometric sensor data, and the like.

[0919] "Support information" refers to advice and guidance regarding pregnancy and childbirth that is generated based on the subject's lifestyle patterns and health status.

[0920] An "emotion recognition engine" is software that analyzes input emotional data and recognizes the emotional state of the subject.

[0921] A "virtual environment" is a virtual reality space that subjects can experience using a head-mounted display or smartphone.

[0922] A "virtual display" is a means of displaying information in a virtual reality space, allowing the subject to intuitively obtain the information.

[0923] "Complement and optimization" refers to using the analyzed emotional data to adjust the support information to the emotional state of the target person and provide it in the most optimal form.

[0924] A "generative AI model" is a model that generates information using artificial intelligence (AI), specifically performing natural language processing and data analysis.

[0925] This invention is a system that allows a subject to receive comprehensive support regarding pregnancy and childbirth, and improves the quality of support by combining an emotion engine. The following describes in detail the mode for carrying out the invention.

[0926] 1. Data Entry and Emotion Recognition

[0927] Users use a smartphone or head-mounted display to input data on their daily life patterns (e.g., meal times, sleep time, activity status) and health status (e.g., weight, blood pressure, presence or absence of morning sickness). Emotional data can also be input using text input, voice input, biosensor data, etc. The device then transmits this data to the server in real time.

[0928] A specific example of its use is when a user enters the time and content of breakfast into the application's food diary, and then reports by voice that they feel "tired" using the emotion engine. This data is sent from the device to the server and stored in a database.

[0929] 2. Data analysis and generation of supporting information

[0930] The server generates support information for pregnancy and childbirth based on the received data. The support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[0931] 3. Sending support information and displaying it in the virtual environment

[0932] The support information generated by the server is displayed intuitively to the user in the virtual environment. The user can efficiently receive the support information via a head-mounted display or smartphone. This information can also be sent via push notifications or email.

[0933] Specific examples

[0934] 1. The user inputs the time and content of meals they had eaten that day, as well as the emotions they felt. Based on this information, the server and emotion engine recommend a lighter meal for the next meal, and if the user feels "tired," it also suggests taking time to rest.

[0935] 2. The user enters their address, number of weeks pregnant, and their feelings. The server and emotion engine search for local subsidy information and guides them on how to apply for the appropriate subsidy. If the user feels anxious, they will also be provided with information on psychological support.

[0936] Prompt Sentence Examples

[0937] "When a user asks 'What to do if morning sickness is severe,' we know that the user is feeling 'worried.' Please provide appropriate advice and suggest ways to relax."

[0938] This allows support information to be generated that takes into account the user's emotional state, enabling intuitive information acquisition in a virtual environment.

[0939] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0940] Step 1:

[0941] The user uses a smartphone or head-mounted display to input lifestyle patterns (e.g., meal times, sleep times, activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biosensor data). This data is sent to the server in real time via the application.

[0942] Input: Life patterns, health status, emotional data

[0943] Output: User data sent to the server

[0944] Specific operation: The user enters various data within the app and presses the send button.

[0945] Step 2:

[0946] The server stores the received data in a database and prepares it for analysis. The stored data is categorized into lifestyle patterns, health status, and emotional data.

[0947] Input: Various data sent by the user

[0948] Output: Classified user data

[0949] Specific behavior: Inserts received data into a database and organizes it by category.

[0950] Step 3:

[0951] The server analyzes the stored data and generates support information for users regarding pregnancy and childbirth, including information on how to deal with morning sickness, health management advice, and how to apply for subsidies.

[0952] Input: Classified user data

[0953] Output: Generated support information

[0954] Specific operation: Executes the analysis algorithm and generates appropriate support information.

[0955] Step 4:

[0956] The server uses an emotion engine to analyze the user's emotional data and complements and optimizes the support information based on the results. For example, if the user feels "fatigue," it will suggest taking time to rest.

[0957] Input: User emotion data and assistance information generated in the previous step

[0958] Output: Optimized support information

[0959] Specific operation: Emotion data is input into the emotion engine and reflected in the support information.

[0960] Step 5:

[0961] The server sends the generated and optimized assistance information to a head-mounted display or smartphone for display in the virtual environment, and also notifies the user of the information via push notification or email as needed.

[0962] Input: Optimized support information

[0963] Output: Assistance information displayed on the user's device

[0964] Specific behavior: Sends assistance information to the appropriate device and converts it into a format for display.

[0965] Step 6:

[0966] Users can view support information in a virtual environment and use it to manage their health and take various steps related to pregnancy, such as checking dietary tips to combat morning sickness or specific procedures for applying for subsidies.

[0967] Input: Assistive information displayed on the device

[0968] Output: User actions and decisions

[0969] Specific actions: Plan and execute actions based on support information.

[0970] The above processing steps provide personalized support information that takes into account the user's emotional state, and enable intuitive acquisition in a virtual environment.

[0971] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0972] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0973] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0974] [Third embodiment]

[0975] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0976] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0977] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[0978] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0979] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0980] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0981] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0982] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0983] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0984] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0985] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0986] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0987] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments of the system are described below.

[0988] 1. Entering information about the subject and receiving data

[0989] The user uses a dedicated application or web portal to enter data on their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and the device then sends this information to the server.

[0990] Examples:

[0991] 1. The user enters the time and type of breakfast they had into the food diary within the application.

[0992] 2. The terminal sends the entered data to the server and stores it in the database.

[0993] 2. Data analysis and generation of supporting information

[0994] Based on the data received by the server, support information related to pregnancy and childbirth is generated, including information on morning sickness, health management advice, and how to apply for subsidies.

[0995] Examples:

[0996] 1. The server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness.

[0997] 2. The server advises the user on the appropriate pace of weight gain based on the weight data entered by the user.

[0998] 3. Sending and displaying support information

[0999] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[1000] Examples:

[1001] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification.

[1002] 2. The user opens the application and views the provided advice.

[1003] Details of each function of the system

[1004] Morning sickness prevention advice

[1005] The server analyzes the user's lifestyle patterns and provides advice on diet and lifestyle changes to alleviate morning sickness, such as adjusting the timing and content of meals and suggesting relaxation methods.

[1006] Examples:

[1007] 1. The user enters the meal times and contents for the day.

[1008] 2. The server uses that information to send you a notification recommending a lighter meal at your next meal.

[1009] Subsidy application support

[1010] The server provides information about available grants based on the user's situation and guides them through the application process, providing a checklist of required documents and deadlines for submission.

[1011] Examples:

[1012] 1. The user enters their address and number of weeks pregnant.

[1013] 2. The server searches for local grant information and sends notifications guiding users on how to apply for relevant grants.

[1014] health care

[1015] The server analyzes weight and blood pressure data entered periodically, and if any of the data deviates from the normal range, it will issue a warning to the user and recommend that they visit an appropriate medical institution.

[1016] Examples:

[1017] 1. The user enters their daily weight and blood pressure.

[1018] 2. The server detects an abnormal value and notifies the user to seek medical attention immediately.

[1019] Storage and sharing of ultrasound images

[1020] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[1021] Examples:

[1022] 1. The user uploads their most recent ultrasound photo to the app.

[1023] 2. The server organizes the photos and generates links for family members to share them.

[1024] Communication Features

[1025] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI to provide appropriate advice and information, creating a situation where users can receive support at any time.

[1026] Examples:

[1027] 1. A user asks within the app, "What should I do if I have severe morning sickness?"

[1028] 2. The server uses an AI engine to analyze the question and respond with advice such as, "Make sure to drink plenty of water and eat light meals."

[1029] The above is an embodiment of the present invention. This system makes it possible to consistently provide a wide range of support to families expecting their first pregnancy.

[1030] The processing flow will be explained below.

[1031] 1. Server-based support for the grant application process

[1032] Step 1:

[1033] The user enters their address and number of weeks pregnant into a dedicated form, and the device sends the entered information to the server.

[1034] Step 2:

[1035] The server receives the submitted information and searches the database for a list of grants based on the region and gestational age.

[1036] Step 3:

[1037] The server obtains detailed information about the subsidy (eligibility criteria, deadline, required documents, etc.) and notifies the user.

[1038] Step 4:

[1039] The user scans and uploads the necessary documents, and the device sends the uploaded documents to the server.

[1040] Step 5:

[1041] The server uses OCR technology to read the document and check the requirements. If there are any errors, the server notifies the user and requests them to make corrections.

[1042] Step 6:

[1043] After verifying that all required documents are received, the server initiates the application process, submits the application to the designated government agency, and notifies the user of successful submission.

[1044] 2. Advice for dealing with morning sickness

[1045] Step 1:

[1046] The user inputs their lifestyle patterns (meal times, stress levels, daily activities), and the device sends the input information to the server.

[1047] Step 2:

[1048] Based on the information sent, the server searches the database for individually appropriate morning sickness prevention advice and optimizes it using an algorithm.

[1049] Step 3:

[1050] The server generates the advice and notifies the user.

[1051] Step 4:

[1052] The user puts the provided advice into practice and provides feedback on the results (whether it was effective or not). The device then sends the feedback information to the server.

[1053] Step 5:

[1054] The server analyzes the feedback information and updates and adjusts the advice as needed. The server notifies the user of new advice on a weekly basis.

[1055] 3. Support for balancing work and family life

[1056] Step 1:

[1057] The user enters information about their work duties, working hours, and the size of the company. The device then sends the input data to the server.

[1058] Step 2:

[1059] Based on the information entered, the server searches a database for information on industry-specific countermeasures and legal regulations.

[1060] Step 3:

[1061] The server provides users with information on maternity and childcare leave systems and workplace solutions.

[1062] Step 4:

[1063] If a user needs tips on communication in the workplace or advice on time management, the user inputs this information. The terminal then sends the input data to the server.

[1064] Step 5:

[1065] The server generates appropriate advice and notifies the user.

[1066] 4. Gathering information on delivery hospitals and support in selecting them

[1067] Step 1:

[1068] The user inputs the conditions of the hospital they want to visit (whether they offer painless childbirth, the facilities, etc.). The terminal sends the input data to the server.

[1069] Step 2:

[1070] The server searches a database of relevant hospitals in the area based on the entered criteria.

[1071] Step 3:

[1072] The server generates a list of candidate hospitals and provides detailed information to the user.

[1073] Step 4:

[1074] The user browses the detailed information of the candidate hospitals and makes a selection. The terminal sends the selection information to the server.

[1075] Step 5:

[1076] The server saves the selected hospital in the user's favorites list and notifies them of the latest information.

[1077] 5. Health management data entry and abnormal value detection

[1078] Step 1:

[1079] The user periodically inputs weight and blood pressure measurement data into the app, and the device sends the input data to the server.

[1080] Step 2:

[1081] The server compares and analyzes the received data with past data.

[1082] Step 3:

[1083] If the server detects an abnormal value, it generates a warning and notifies the user.

[1084] Step 4:

[1085] The user receives a notification of an abnormal value and inputs a request for information on how to respond. The device sends the input data to the server.

[1086] Step 5:

[1087] The server searches for nearby medical institutions and provides a list of appropriate medical institutions.

[1088] 6. Digital storage and sharing of ultrasound images

[1089] Step 1:

[1090] The user scans the Echo photo and uploads it using the device, which then sends the uploaded photo to the server.

[1091] Step 2:

[1092] The server organizes the photos by date and displays them in an album format.

[1093] Step 3:

[1094] The user selects who (family, friends) they want to share the photos with, and the device sends the selection information to the server.

[1095] Step 4:

[1096] The server generates a shared link and notifies the specified recipient.

[1097] 7. Consultation function as a communication partner

[1098] Step 1:

[1099] The user inputs a question into the chatbot within the app, and the device sends the input data to the server.

[1100] Step 2:

[1101] The server uses an AI engine to analyze the question and find the appropriate answer.

[1102] Step 3:

[1103] The server notifies the user of the generated answer.

[1104] Step 4:

[1105] The user enters an additional question, and the device sends the input data to the server again.

[1106] Step 5:

[1107] The server reparses and generates successively appropriate answers.

[1108] Example 1

[1109] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1110] Families expecting their first pregnancy need information and support regarding pregnancy and childbirth, but there are limited ways to receive appropriate advice and support. In particular, important information such as measures to combat morning sickness and how to apply for subsidies needs to be tailored to each individual's situation. For this reason, a system that can provide efficient and comprehensive support is needed.

[1111] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1112] In this invention, the server includes a means for inputting data on the lifestyle patterns and health conditions of the subject, a means for transmitting the data from a terminal to the server, a server equipped with a data analysis means for generating support information related to pregnancy and childbirth based on the data, and a means for transmitting the support information from the server to the terminal and notifying the user. This makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[1113] "Subject's lifestyle patterns" refers to the totality of an individual's daily behaviors and habits, including meal times, sleep times, and daily activity status.

[1114] "Health status" refers to data that indicates an individual's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[1115] "Means for inputting data" refers to the means by which users input information about their lifestyle patterns and health status, such as using a dedicated application or web portal.

[1116] "Means for transmitting data from a terminal to a server" refers to a communication means for transmitting data from a terminal (e.g., smartphone, PC) to a server via the Internet.

[1117] "Data analysis means" refers to algorithms and software that analyze and process data in order to generate support information related to pregnancy and childbirth based on the received data.

[1118] "Support information" refers to a comprehensive range of information related to pregnancy and childbirth, such as measures to prevent morning sickness, health management advice, and how to apply for subsidies.

[1119] "Server" refers to a computer system for storing data, analyzing data, generating and transmitting support information.

[1120] "Terminal" refers to a device used by a user to input and receive data, such as a smartphone or computer.

[1121] "Means of notification" refers to methods such as push notification, email, or in-app notification for notifying the user of the generated support information.

[1122] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments are described below.

[1123] First, users access a dedicated application or web portal and enter data about their lifestyle and health conditions, including meal times, sleep time, daily activity levels, weight, blood pressure, and whether they experience morning sickness. This data is entered from the user's smartphone, computer, or other device.

[1124] Next, the terminal sends the input data to a server via the Internet. At this time, the data is sent safely using a secure communication method. The server receives the data and stores it in a database.

[1125] The server analyzes the received data using a generative AI model (e.g., GPT-3 or BERT) to generate personalized support information based on the user's lifestyle and health status. This support information covers a wide range of topics, including advice on morning sickness, health management, and how to apply for subsidies.

[1126] As a specific example, if a user inputs the time and content of their breakfast, such as "I had toast and orange juice at 7:30," the server will analyze that information and generate specific dietary advice to alleviate morning sickness, such as "I recommend having some easily digestible fruit and light protein for your next breakfast."

[1127] The generated support information is sent from the server to the user's device. Notifications can be sent in various ways, such as push notifications, emails, and in-app notifications. For example, advice on how to deal with morning sickness can be sent as a push notification, and when the user taps the notification to open the application, detailed advice will be displayed.

[1128] An example of a prompt sentence would be, "What should I do if I have severe morning sickness?" In this case, the server will analyze it using an AI engine and respond with specific advice such as, "Try to drink plenty of fluids and eat light meals."

[1129] This system makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] Users access a dedicated application or web portal and enter their lifestyle patterns and health status. Data entered includes meal times, meal contents, sleep time, daily activity status, weight, blood pressure, and whether or not they have morning sickness. For example, users enter information such as "I had toast and orange juice for breakfast at 7:30." This information is saved on the device as initial data.

[1133] Step 2:

[1134] The device sends the entered data to the server. The data is sent via the Internet using a secure communication method. Specifically, the data sent includes the meal details and time entered by the user, and the information "I had toast and orange juice at 7:30" is transferred. The server receives this data and stores it in a database.

[1135] Step 3:

[1136] The server analyzes the received data. Based on the received data, a generative AI model (e.g., GPT-3 or BERT) is used to understand the user's lifestyle patterns and health status. Here, data from the past few weeks is statistically analyzed to identify trends such as "irregular breakfast times." This allows for a deeper understanding of the user's lifestyle patterns.

[1137] Step 4:

[1138] The server generates support information for pregnancy and childbirth based on the analysis results. Based on the lifestyle patterns and health condition data obtained from the analysis, the generative AI model generates appropriate advice and support information. For example, the generated advice might be, "We recommend that you eat easily digestible fruit and light protein for your next breakfast." The generated support information is temporarily stored on the server.

[1139] Step 5:

[1140] The server sends the generated support information to the user's device. Notification methods can be selected from a variety of methods, including push notifications, emails, and in-app notifications. A specific example of how this works is a notification such as, "To alleviate morning sickness, we recommend eating easily digestible fruit and light protein for breakfast."

[1141] Step 6:

[1142] The user receives a notification on their device and opens the app or web portal to view the support information. When the user taps the notification on their smartphone, detailed advice is displayed within the app, allowing the user to review the support information provided and incorporate it into their daily life.

[1143] Through the above processing steps, efficient and timely support information is provided to families expecting their first pregnancy.

[1144] (Application example 1)

[1145] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1146] Families expecting their first pregnancy need comprehensive support regarding pregnancy and childbirth, but current systems lack information tailored to each individual user. Furthermore, there is no mechanism in place to provide the necessary content in a timely manner, making it difficult for information recipients to receive the necessary support at the appropriate time. This creates a problem in which users are unable to feel at ease during their pregnancy.

[1147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1148] In this invention, the server includes a means for receiving data on the lifestyle patterns and health conditions of a subject, a means for generating support information related to pregnancy and childbirth based on the data, a means for generating the support information as content suitable for each individual, and a means for delivering the content to the user, thereby enabling personalized content optimized for each individual user to be delivered in a timely manner.

[1149] "Covered Person" refers to an individual or family member who needs assistance with pregnancy and childbirth.

[1150] "Lifestyle patterns" refers to the subject's daily habits and behaviors, such as meal times, sleep times, and activity levels.

[1151] "Health status" refers to the subject's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[1152] "Data receiving means" refers to a device or method for acquiring data on the subject's lifestyle patterns and health status and inputting it into the system.

[1153] "Support information generation means" refers to a device or method that generates appropriate advice and information regarding pregnancy and childbirth based on the received data.

[1154] The "content generation means" refers to a device or method for creating the generated support information as content in a form optimized for each individual target person.

[1155] "Content distribution means" refers to a device or method for distributing generated content to a target person in a timely manner.

[1156] "Morning sickness advice" refers to information that includes specific suggestions for dietary and lifestyle changes to reduce morning sickness symptoms.

[1157] "Subsidy application support information" refers to details of subsidies available to eligible persons and support information regarding the application procedures.

[1158] MODE FOR CARRYING OUT THE INVENTION

[1159] The present invention provides a system for providing comprehensive support for pregnancy and childbirth to a subject. Specific embodiments of the system are described below.

[1160] The system has the following main functions:

[1161] 1. Entering information about the subject and receiving data

[1162] Users enter data about their lifestyle and health status via a dedicated application or web portal, including meal times, sleep time, activity level, weight, blood pressure, and whether they suffer from morning sickness. The data entered by the user is sent to a server via the device, where it is stored in the system's central database.

[1163] As a specific example, when a user enters the time and content of breakfast into an application, the device sends the information to a server and stores it in a database.

[1164] 2. Data analysis and generation of supporting information

[1165] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes advice on how to deal with morning sickness, health management, and assistance with applying for subsidies. Furthermore, the support information is generated as personalized content and provided in the most appropriate format for each individual user.

[1166] As a specific example, the server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness. It also advises the user on the appropriate pace of weight gain based on their weight data.

[1167] 3. Sending and displaying support information

[1168] The server notifies the user of the generated assistance information and displays it through the application or web portal. Notifications can be made via push notifications, emails, or in-application notifications. The user can then use their device to review the received assistance information and adjust their actions accordingly.

[1169] For example, the server generates morning sickness advice and sends it to the user's smartphone as a push notification. When the user opens the application, they can view the advice.

[1170] Hardware and software used

[1171] Smartphone: Used as a device for entering information and receiving notifications.

[1172] Server: Used as a backend server for data analysis, support information generation, and notification distribution. For example, AWS or Google Cloud Platform can be used.

[1173] AI engines: Used for data analysis and content generation, specifically generative AI models such as TensorFlow and PyTorch.

[1174] Examples of specific examples and prompts

[1175] Below are examples of specific prompt sentences to be fed into the generative AI model.

[1176] Analyze the user's lifestyle and health data to generate recommended content for the day, such as dietary advice to alleviate morning sickness or exercise advice for weight management.

[1177] By using such prompts, the AI ​​engine can generate appropriate support information and provide it to the user in a personalized format, enabling the system to provide the necessary information at the appropriate time during pregnancy.

[1178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] A user uses a dedicated application or web portal to input data about their lifestyle and health status (e.g., meal times, sleep time, activity level, weight, blood pressure, presence or absence of morning sickness). The input data is received by the device. The input in this step is data about the user's lifestyle and health status, and the output is data stored in the device.

[1181] Step 2:

[1182] The terminal sends the data entered by the user to the server. Specifically, the terminal sends the data to the specified endpoint via the network. The input is the data stored on the terminal, and the output is the data received by the server.

[1183] Step 3:

[1184] The server analyzes the data it receives. Specifically, the server uses an AI engine to generate support information for pregnancy and childbirth based on the user's lifestyle patterns and health status data. At this time, personalized content is generated by a generative AI model. The input is the data received by the server, and the output is the generated support information.

[1185] Step 4:

[1186] Based on the support information generated by the server, the server generates optimal content for each individual user. Specifically, content such as advice on how to deal with morning sickness and health management is created based on the results obtained from the AI ​​engine. The input is analyzed data, and the output is personalized content.

[1187] Step 5:

[1188] The server notifies the user of the generated content. Specifically, the server sends a push notification, email, or in-app notification to the device. The input is the generated content, and the output is the notification to the user.

[1189] Step 6:

[1190] The user receives a notification and opens an application or web portal to view supporting information. Specifically, the user reviews the content within the app and adjusts their behavior accordingly. The input is the received notification and the output is the user action.

[1191] In this way, the entire system works together to provide users with comprehensive and personalized support regarding pregnancy and childbirth.

[1192] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1193] This invention is a system that provides comprehensive support regarding pregnancy and childbirth to families expecting their first pregnancy, and by combining it with an emotion engine that recognizes the user's emotions, it can provide even more comprehensive support.

[1194] 1. Entering information about the subject and receiving data

[1195] The user uses a dedicated application or web portal to input their lifestyle patterns (e.g., meal times, sleep times, daily activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biometric sensor data, etc.). The device then sends this information to the server.

[1196] Examples:

[1197] 1. The user enters the time and contents of breakfast into the food diary within the application, and the emotion engine reports via voice input that they feel "tired."

[1198] 2. The device sends the input data and voice data to the server and stores them in a database.

[1199] 2. Data analysis and generation of supporting information

[1200] Based on the data received by the server, it generates support information related to pregnancy and childbirth. Support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[1201] Examples:

[1202] 1. The server analyzes the user's eating patterns and emotional data and generates dietary advice to alleviate morning sickness. If the user feels "fatigue," the server generates advice that takes this into account.

[1203] 2. The server advises the user on the appropriate pace of weight gain based on the weight and emotional data entered by the user. If the user feels "anxious," it also suggests ways to relax.

[1204] 3. Sending and displaying support information

[1205] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[1206] Examples:

[1207] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification. If the user feels "fatigue," it also sends a notification encouraging them to take a rest.

[1208] 2. The user opens the application and views the provided advice.

[1209] More morning sickness advice

[1210] The server and emotion engine work together to analyze the user's lifestyle patterns and emotional data, and provide advice on dietary and lifestyle changes to alleviate morning sickness.

[1211] Examples:

[1212] 1. The user inputs the times and contents of meals they ate each day, as well as the emotions they felt.

[1213] 2. Based on this information, the server and emotion engine will recommend a lighter meal for the next meal and, if the user feels "tired," will also suggest taking time to rest.

[1214] Subsidy application support details

[1215] The server and emotion engine work together to provide applicable grant information and guide the application process based on the user's situation and emotion data. It also provides a checklist of required documents and deadlines for submission. If the user feels anxious or stressed, it also provides psychological support information.

[1216] Examples:

[1217] 1. The user enters their address, how many weeks pregnant they are, and how they feel.

[1218] 2. The server and emotion engine search for local subsidy information and guide users on how to apply for the appropriate subsidy, while also providing psychological support information if the user feels "anxious."

[1219] Health Management Details

[1220] The server analyzes weight and blood pressure data entered periodically and issues a warning if the data falls outside the normal range. Furthermore, the emotion engine analyzes the emotional data and suggests appropriate relaxation methods or visiting a medical institution if the user is feeling anxious or stressed.

[1221] Examples:

[1222] 1. The user enters their daily weight, blood pressure, and emotional data.

[1223] 2. The server and emotion engine detect abnormal values, notify the user to seek medical attention immediately, and suggest relaxation methods.

[1224] Learn more about storing and sharing ultrasound photos

[1225] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[1226] Examples:

[1227] 1. The user uploads their most recent ultrasound photo to the app.

[1228] 2. The server organizes the photos and generates links for family members to share them.

[1229] Details of the consultation function as a communication partner

[1230] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI and an emotion engine to provide appropriate advice and information, thereby providing even greater support.

[1231] Examples:

[1232] 1. When users ask questions in the app such as "What should I do if I have severe morning sickness?", they also input the emotions they are feeling.

[1233] 2. The server and emotion engine analyze the question and respond with advice such as "Make sure to drink plenty of water and eat light meals," and if the user is "worried," it also suggests further ways to relax.

[1234] The above is a detailed embodiment for carrying out the present invention. By utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be realized for families experiencing their first pregnancy.

[1235] The processing flow will be explained below.

[1236] Processing flow of a system that combines emotion engines

[1237] 1. Information input and data reception

[1238] Step 1:

[1239] Users enter their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness) into a dedicated application or web portal, along with emotional data (e.g., text input, voice input, biometric sensor data, etc.).

[1240] Step 2:

[1241] The terminal transmits the information entered by the user to the server.

[1242] Step 3:

[1243] The server stores the received information in a database, and sends the emotion data to the emotion engine.

[1244] 2. Data analysis and generation of supporting information

[1245] Step 1:

[1246] The server analyzes the stored lifestyle patterns, health status, and emotional data.

[1247] Step 2:

[1248] The emotion engine analyzes the emotion data and identifies the user's current emotional state (e.g., "tired," "anxious," "stressed").

[1249] Step 3:

[1250] The server generates support information for pregnancy and childbirth based on the analysis results, and also reflects the analysis results of the emotion engine.

[1251] 3. Sending and displaying support information

[1252] Step 1:

[1253] The server notifies the user of the assistance information generated by the server via push notification, email, or in-app notification.

[1254] Step 2:

[1255] The user opens the application and views the notified support information.

[1256] Specific function processing flow

[1257] Morning sickness prevention advice

[1258] Step 1:

[1259] Users enter the time and content of their meals throughout the day, as well as the emotions they are feeling, into the app.

[1260] Step 2:

[1261] The device sends input data and emotion data to the server.

[1262] Step 3:

[1263] The server analyzes the data and generates dietary advice to alleviate morning sickness.

[1264] Step 4:

[1265] The emotion engine analyzes the emotional data and, if the user is feeling "tired," generates advice that takes that state into account.

[1266] Step 5:

[1267] The server notifies the user of the generated advice.

[1268] Subsidy application support

[1269] Step 1:

[1270] The user enters their address, how many weeks pregnant they are, and how they are feeling.

[1271] Step 2:

[1272] The terminal transmits the input data and emotion data to the server.

[1273] Step 3:

[1274] The server searches for local grant information and generates notifications guiding users on how to apply for the appropriate grant.

[1275] Step 4:

[1276] The emotion engine analyzes the user's emotional data, and if the user is feeling "anxiety," it also generates mental support information that takes that state into account.

[1277] Step 5:

[1278] The server generates grant application guides and provides moral support information to the user.

[1279] Health management data analysis and outlier detection

[1280] Step 1:

[1281] Users periodically enter their weight, blood pressure, and emotional data into the app.

[1282] Step 2:

[1283] The terminal sends the input data to the server.

[1284] Step 3:

[1285] The server analyzes the data and generates alerts if outliers are detected.

[1286] Step 4:

[1287] The emotion engine analyzes the emotional data and, if the user is feeling "worried" or "stressed," suggests relaxation methods or visiting a medical institution that take that state into consideration.

[1288] Step 5:

[1289] The server will warn the user about the abnormal value and inform them how to relax.

[1290] Storage and sharing of ultrasound images

[1291] Step 1:

[1292] The user scans the ultrasound photo and uploads it using the device.

[1293] Step 2:

[1294] The device sends the uploaded photos to the server.

[1295] Step 3:

[1296] The server organizes the photos by date and displays them in an album format.

[1297] Step 4:

[1298] The user selects who (family, friends) they want to share the photos with.

[1299] Step 5:

[1300] The terminal transmits the selection information to the server.

[1301] Step 6:

[1302] The server generates a shared link and notifies the specified recipient.

[1303] Consultation function as a communication partner

[1304] Step 1:

[1305] Users input questions into the in-app chatbot, along with their emotional state.

[1306] Step 2:

[1307] The terminal sends the input data to the server.

[1308] Step 3:

[1309] The server uses an AI engine and an emotion engine to analyze the question and emotion data and search for the appropriate answer.

[1310] Step 4:

[1311] The server notifies the user of the generated answer, along with additional advice based on the user's emotional state.

[1312] Step 5:

[1313] The user enters a follow-up question.

[1314] Step 6:

[1315] The terminal again sends the input data to the server.

[1316] Step 7:

[1317] The server re-parses and subsequently generates the appropriate answer and notifies the user.

[1318] The above is a specific implementation of a system that combines an emotion engine. This processing flow allows users to receive more personalized support for pregnancy and childbirth.

[1319] Example 2

[1320] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1321] The challenge is to provide comprehensive support for first-time pregnant families regarding pregnancy and childbirth while providing personalized advice tailored to each user's emotional state. There is also a need to effectively utilize the information users input daily to provide the necessary support in real time.

[1322] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1323] In this invention, the server includes means for receiving data on the subject's lifestyle patterns, health status, and emotions, means for generating support information related to pregnancy and childbirth based on the data and for analyzing the emotion data to complement and optimize the support information, means for transmitting the support information and displaying it through an application or web portal, means for organizing and storing image data uploaded by users in chronological order, and means for users to input questions and provide appropriate advice using artificial intelligence and emotion analysis technology, thereby enabling users to efficiently receive a variety of support information tailored to their individual situations and emotions.

[1324] "Target individuals" are those who use this system to receive support regarding pregnancy and childbirth.

[1325] "Lifestyle patterns" refers to data such as meal times, sleep times, and daily activity status in the subject's daily life.

[1326] "Health status" refers to data related to the subject's health, such as weight, blood pressure, and whether or not they have morning sickness.

[1327] "Emotion data" refers to data related to emotions obtained from text, voice, biometric sensor data, etc. input by the subject.

[1328] A "server" is a computer system that processes received data and generates and transmits assistance information.

[1329] The "emotion engine" is a technology that analyzes emotional data and uses the results to complement and optimize support information.

[1330] "Support information" includes advice on pregnancy and childbirth, measures to prevent morning sickness, health management suggestions, and procedures for applying for subsidies.

[1331] An "application" is software used by a subject to enter information and receive assistance information.

[1332] A "web portal" is a website where subjects can enter information via a browser and receive support information.

[1333] "Image data" refers to image files uploaded by the subject, such as ultrasound images.

[1334] "Artificial intelligence" refers to technologies such as machine learning and natural language processing that analyze data and provide appropriate advice.

[1335] This invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. In particular, the quality of support is improved by combining it with an emotion engine that recognizes the user's emotions.

[1336] First, the user uses a dedicated application or web portal to input their lifestyle patterns (meal times, sleep times, daily activity status), health status (weight, blood pressure, presence or absence of morning sickness), and emotional data (text input, voice input, biosensor data, etc.). The terminals used here are mobile devices such as smartphones and tablets.

[1337] The device then sends this information to the server, which uses the HTTPS protocol to transfer data securely. The server then stores the received data in a MariaDB database.

[1338] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes information on morning sickness, health management advice, and how to apply for subsidies. Technically, Python scripts are used for data analysis to generate the support information. The emotion engine also uses a generative AI model (e.g., the BERT model) to analyze the user's emotion data. Here, the emotion engine converts the voice data into text and extracts the user's emotion.

[1339] The generated support information is notified to the user by the server via push notification, email, or in-app notification, which is displayed on the user's smartphone or tablet.

[1340] To give a specific example, the user can input the time and contents of breakfast and how "tired" they feel by voice. The device sends this to the server, and the data is saved in a database. Based on the received data, the server generates dietary advice to alleviate morning sickness, reflecting the analysis results of the emotion engine and providing the user with advice such as "We recommend a light breakfast and ensure you get plenty of rest."

[1341] Users can also upload ultrasound images, which the server receives and stores in a database organized by date. If desired, a link can be generated to share the images with family and friends.

[1342] Furthermore, when questions or concerns about pregnancy are entered into the chatbot within the app, the server uses AI and an emotion engine to provide appropriate advice and information. For example, in response to the question, "What should I do if I have severe morning sickness?", the server will respond with, "Make sure to drink plenty of water and eat light meals." If the user is "worried," it will also suggest ways to relax.

[1343] An example of a prompt is as follows:

[1344] "I had bread and eggs at eight o'clock. I'm very tired now."

[1345] "What should I do if I have severe morning sickness?"

[1346] In this way, by utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be provided to families experiencing their first pregnancy.

[1347] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1348] Step 1:

[1349] The user launches a dedicated application or web portal and enters data on their lifestyle, health status, and emotions.

[1350] Specific actions: The user opens the app on their smartphone, taps the "Meal Log" button, and then voice-records that they ate bread and eggs for breakfast at 8 a.m. and feel "tired."

[1351] Input: Lifestyle patterns (e.g., meal times, sleep times), health status (e.g., weight, blood pressure), emotional data (text input, voice input)

[1352] Output: The input data is saved on the device.

[1353] Step 2:

[1354] The terminal transmits the input data to the server.

[1355] Specific operation: The terminal converts the input information into packets in real time and sends them securely to the server using the HTTPS protocol.

[1356] Input: User-entered lifestyle patterns, health status, and emotional data

[1357] Output: The data is sent to the server.

[1358] Step 3:

[1359] The server stores the received data in a database.

[1360] Specific operation: The server receives the received information and stores it in the MariaDB database system.

[1361] Input: Data sent from the terminal

[1362] Output: The data is saved to a database.

[1363] Step 4:

[1364] The server analyzes the received data and generates support information, including information on morning sickness, health management advice, and how to apply for subsidies.

[1365] How it works: The server uses a Python script to analyze the received dietary and emotional data and generate dietary advice to alleviate morning sickness. The emotion engine uses the BERT model to convert the audio data into text and extract the emotion "fatigue." Based on this information, it recommends a light breakfast and encourages the user to get some rest.

[1366] Input: User data stored in the database

[1367] Output: Generated support information

[1368] Step 5:

[1369] The server notifies the user of the generated support information.

[1370] Specific operation: The server prepares the generated dietary advice and rest notification and sends it as a push notification. The device receives the notification and displays a message such as "To alleviate morning sickness, we recommend having a light breakfast next time" in the notification bar of the user's smartphone.

[1371] Input: Generated support information

[1372] Output: Sent to the user as a notification.

[1373] Step 6:

[1374] The user receives the notified advice.

[1375] What happens: The user taps on the notification on their smartphone and sees detailed advice in the app.

[1376] Input: Notified support information

[1377] Output: The user views and acts on the advice.

[1378] Step 7:

[1379] Users upload ultrasound photos, which are then stored on a server in chronological order, and if desired, a link is generated to share with family and friends.

[1380] Specific operation: The user taps the "Upload Echo Photo" button, selects and uploads a photo. The server receives the photo, organizes it by date, and stores it in a database. If desired, a sharing link is generated and notified to the user.

[1381] Input: Ultrasound image data

[1382] Output: Organized photo data and a shareable link

[1383] Step 8:

[1384] Users input their questions and concerns into the in-app chatbot, and the server uses AI and an emotion engine to provide appropriate advice.

[1385] Specific operation: When a user asks a question in the app, such as "What should I do if I have severe morning sickness?", they also input their emotions. The server and emotion engine analyze the question and emotion data, and respond with advice such as "Make sure to drink plenty of water and eat light meals." If the user is "worried," the app also suggests further relaxation methods.

[1386] Input: Question content and emotion data

[1387] Output: Good advice

[1388] (Application example 2)

[1389] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1390] Families expecting their first child often need a lot of information about pregnancy and childbirth, and in particular emotional support. However, conventional support systems lack the ability to provide support information that takes into account each individual's emotional state, and provide intuitive information acquisition methods using virtual environments, which means they are unable to fully alleviate users' anxiety and stress.

[1391] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data on the subject's lifestyle patterns and health condition, means for generating support information related to pregnancy and childbirth based on the data, means for analyzing emotion data and complementing and optimizing the support information, means for displaying the generated support information in a virtual environment, and means for transmitting the support information. This makes it possible for the user to intuitively obtain personalized support information in a virtual environment that takes into account the user's emotional state, thereby reducing anxiety and stress.

[1392] "Target" refers to users who use this system to receive support regarding pregnancy and childbirth.

[1393] "Lifestyle patterns" refers to the subject's daily habits and behaviors, specifically including meal times, sleep times, daily activity status, etc.

[1394] "Health condition" is information that indicates the physical condition of the subject, and specifically includes weight, blood pressure, whether or not the subject has morning sickness, and the like.

[1395] "Emotion data" is information that indicates the emotional state of a subject, and specifically includes text input, voice input, biometric sensor data, and the like.

[1396] "Support information" refers to advice and guidance regarding pregnancy and childbirth that is generated based on the subject's lifestyle patterns and health status.

[1397] An "emotion recognition engine" is software that analyzes input emotional data and recognizes the emotional state of the subject.

[1398] A "virtual environment" is a virtual reality space that subjects can experience using a head-mounted display or smartphone.

[1399] A "virtual display" is a means of displaying information in a virtual reality space, allowing the subject to intuitively obtain the information.

[1400] "Complement and optimization" refers to using the analyzed emotional data to adjust the support information to the emotional state of the target person and provide it in the most optimal form.

[1401] A "generative AI model" is a model that generates information using artificial intelligence (AI), specifically performing natural language processing and data analysis.

[1402] This invention is a system that allows a subject to receive comprehensive support regarding pregnancy and childbirth, and improves the quality of support by combining an emotion engine. The following describes in detail the mode for carrying out the invention.

[1403] 1. Data Entry and Emotion Recognition

[1404] Users use a smartphone or head-mounted display to input data on their daily life patterns (e.g., meal times, sleep time, activity status) and health status (e.g., weight, blood pressure, presence or absence of morning sickness). Emotional data can also be input using text input, voice input, biosensor data, etc. The device then transmits this data to the server in real time.

[1405] A specific example of its use is when a user enters the time and content of breakfast into the application's food diary, and then reports by voice that they feel "tired" using the emotion engine. This data is sent from the device to the server and stored in a database.

[1406] 2. Data analysis and generation of supporting information

[1407] The server generates support information for pregnancy and childbirth based on the received data. The support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[1408] 3. Sending support information and displaying it in the virtual environment

[1409] The support information generated by the server is displayed intuitively to the user in the virtual environment. The user can efficiently receive the support information via a head-mounted display or smartphone. This information can also be sent via push notifications or email.

[1410] Specific examples

[1411] 1. The user inputs the time and content of meals they had eaten that day, as well as the emotions they felt. Based on this information, the server and emotion engine recommend a lighter meal for the next meal, and if the user feels "tired," it also suggests taking time to rest.

[1412] 2. The user enters their address, number of weeks pregnant, and their feelings. The server and emotion engine search for local subsidy information and guides them on how to apply for the appropriate subsidy. If the user feels anxious, they will also be provided with information on psychological support.

[1413] Prompt Sentence Examples

[1414] "When a user asks 'What to do if morning sickness is severe,' we know that the user is feeling 'worried.' Please provide appropriate advice and suggest ways to relax."

[1415] This allows support information to be generated that takes into account the user's emotional state, enabling intuitive information acquisition in a virtual environment.

[1416] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1417] Step 1:

[1418] The user uses a smartphone or head-mounted display to input lifestyle patterns (e.g., meal times, sleep times, activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biosensor data). This data is sent to the server in real time via the application.

[1419] Input: Life patterns, health status, emotional data

[1420] Output: User data sent to the server

[1421] Specific operation: The user enters various data within the app and presses the send button.

[1422] Step 2:

[1423] The server stores the received data in a database and prepares it for analysis. The stored data is categorized into lifestyle patterns, health status, and emotional data.

[1424] Input: Various data sent by the user

[1425] Output: Classified user data

[1426] Specific behavior: Inserts received data into a database and organizes it by category.

[1427] Step 3:

[1428] The server analyzes the stored data and generates support information for users regarding pregnancy and childbirth, including information on how to deal with morning sickness, health management advice, and how to apply for subsidies.

[1429] Input: Classified user data

[1430] Output: Generated support information

[1431] Specific operation: Executes the analysis algorithm and generates appropriate support information.

[1432] Step 4:

[1433] The server uses an emotion engine to analyze the user's emotional data and complements and optimizes the support information based on the results. For example, if the user feels "fatigue," it will suggest taking time to rest.

[1434] Input: User emotion data and assistance information generated in the previous step

[1435] Output: Optimized support information

[1436] Specific operation: Emotion data is input into the emotion engine and reflected in the support information.

[1437] Step 5:

[1438] The server sends the generated and optimized assistance information to a head-mounted display or smartphone for display in the virtual environment, and also notifies the user of the information via push notification or email as needed.

[1439] Input: Optimized support information

[1440] Output: Assistance information displayed on the user's device

[1441] Specific behavior: Sends assistance information to the appropriate device and converts it into a format for display.

[1442] Step 6:

[1443] Users can view support information in a virtual environment and use it to manage their health and take various steps related to pregnancy, such as checking dietary tips to combat morning sickness or specific procedures for applying for subsidies.

[1444] Input: Assistive information displayed on the device

[1445] Output: User actions and decisions

[1446] Specific actions: Plan and execute actions based on support information.

[1447] The above processing steps provide personalized support information that takes into account the user's emotional state, and enable intuitive acquisition in a virtual environment.

[1448] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1449] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1450] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1451] [Fourth embodiment]

[1452] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1453] 7, a 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.

[1454] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. 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. The database 24 and the communication I / F 26 are also 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).

[1455] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1456] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1458] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1459] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1460] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1461] The specific processing program 56 is an example of a "program" according to the technology of the present 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 in accordance with the specific processing program 56 executed on the RAM 30.

[1462] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1463] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1464] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1465] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments of the system are described below.

[1466] 1. Entering information about the subject and receiving data

[1467] The user uses a dedicated application or web portal to enter data on their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and the device then sends this information to the server.

[1468] Examples:

[1469] 1. The user enters the time and type of breakfast they had into the food diary within the application.

[1470] 2. The terminal sends the entered data to the server and stores it in the database.

[1471] 2. Data analysis and generation of supporting information

[1472] Based on the data received by the server, support information related to pregnancy and childbirth is generated, including information on morning sickness, health management advice, and how to apply for subsidies.

[1473] Examples:

[1474] 1. The server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness.

[1475] 2. The server advises the user on the appropriate pace of weight gain based on the weight data entered by the user.

[1476] 3. Sending and displaying support information

[1477] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[1478] Examples:

[1479] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification.

[1480] 2. The user opens the application and views the provided advice.

[1481] Details of each function of the system

[1482] Morning sickness prevention advice

[1483] The server analyzes the user's lifestyle patterns and provides advice on diet and lifestyle changes to alleviate morning sickness, such as adjusting the timing and content of meals and suggesting relaxation methods.

[1484] Examples:

[1485] 1. The user enters the meal times and contents for the day.

[1486] 2. The server uses that information to send you a notification recommending a lighter meal at your next meal.

[1487] Subsidy application support

[1488] The server provides information about available grants based on the user's situation and guides them through the application process, providing a checklist of required documents and deadlines for submission.

[1489] Examples:

[1490] 1. The user enters their address and number of weeks pregnant.

[1491] 2. The server searches for local grant information and sends notifications guiding users on how to apply for relevant grants.

[1492] health care

[1493] The server analyzes weight and blood pressure data entered periodically, and if any of the data deviates from the normal range, it will issue a warning to the user and recommend that they visit an appropriate medical institution.

[1494] Examples:

[1495] 1. The user enters their daily weight and blood pressure.

[1496] 2. The server detects an abnormal value and notifies the user to seek medical attention immediately.

[1497] Storage and sharing of ultrasound images

[1498] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[1499] Examples:

[1500] 1. The user uploads their most recent ultrasound photo to the app.

[1501] 2. The server organizes the photos and generates links for family members to share them.

[1502] Communication Features

[1503] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI to provide appropriate advice and information, creating a situation where users can receive support at any time.

[1504] Examples:

[1505] 1. A user asks within the app, "What should I do if I have severe morning sickness?"

[1506] 2. The server uses an AI engine to analyze the question and respond with advice such as, "Make sure to drink plenty of water and eat light meals."

[1507] The above is an embodiment of the present invention. This system makes it possible to consistently provide a wide range of support to families expecting their first pregnancy.

[1508] The processing flow will be explained below.

[1509] 1. Server-based support for the grant application process

[1510] Step 1:

[1511] The user enters their address and number of weeks pregnant into a dedicated form, and the device sends the entered information to the server.

[1512] Step 2:

[1513] The server receives the submitted information and searches the database for a list of grants based on the region and gestational age.

[1514] Step 3:

[1515] The server obtains detailed information about the subsidy (eligibility criteria, deadline, required documents, etc.) and notifies the user.

[1516] Step 4:

[1517] The user scans and uploads the necessary documents, and the device sends the uploaded documents to the server.

[1518] Step 5:

[1519] The server uses OCR technology to read the document and check the requirements. If there are any errors, the server notifies the user and requests them to make corrections.

[1520] Step 6:

[1521] After verifying that all required documents are received, the server initiates the application process, submits the application to the designated government agency, and notifies the user of successful submission.

[1522] 2. Advice for dealing with morning sickness

[1523] Step 1:

[1524] The user inputs their lifestyle patterns (meal times, stress levels, daily activities), and the device sends the input information to the server.

[1525] Step 2:

[1526] Based on the information sent, the server searches the database for individually appropriate morning sickness prevention advice and optimizes it using an algorithm.

[1527] Step 3:

[1528] The server generates the advice and notifies the user.

[1529] Step 4:

[1530] The user puts the provided advice into practice and provides feedback on the results (whether it was effective or not). The device then sends the feedback information to the server.

[1531] Step 5:

[1532] The server analyzes the feedback information and updates and adjusts the advice as needed. The server notifies the user of new advice on a weekly basis.

[1533] 3. Support for balancing work and family life

[1534] Step 1:

[1535] The user enters information about their work duties, working hours, and the size of the company. The device then sends the input data to the server.

[1536] Step 2:

[1537] Based on the information entered, the server searches a database for information on industry-specific countermeasures and legal regulations.

[1538] Step 3:

[1539] The server provides users with information on maternity and childcare leave systems and workplace solutions.

[1540] Step 4:

[1541] If a user needs tips on communication in the workplace or advice on time management, the user inputs this information. The terminal then sends the input data to the server.

[1542] Step 5:

[1543] The server generates appropriate advice and notifies the user.

[1544] 4. Gathering information on delivery hospitals and support in selecting them

[1545] Step 1:

[1546] The user inputs the conditions of the hospital they want to visit (whether they offer painless childbirth, the facilities, etc.). The terminal sends the input data to the server.

[1547] Step 2:

[1548] The server searches a database of relevant hospitals in the area based on the entered criteria.

[1549] Step 3:

[1550] The server generates a list of candidate hospitals and provides detailed information to the user.

[1551] Step 4:

[1552] The user browses the detailed information of the candidate hospitals and makes a selection. The terminal sends the selection information to the server.

[1553] Step 5:

[1554] The server saves the selected hospital in the user's favorites list and notifies them of the latest information.

[1555] 5. Health management data entry and abnormal value detection

[1556] Step 1:

[1557] The user periodically inputs weight and blood pressure measurement data into the app, and the device sends the input data to the server.

[1558] Step 2:

[1559] The server compares and analyzes the received data with past data.

[1560] Step 3:

[1561] If the server detects an abnormal value, it generates a warning and notifies the user.

[1562] Step 4:

[1563] The user receives a notification of an abnormal value and inputs a request for information on how to respond. The device sends the input data to the server.

[1564] Step 5:

[1565] The server searches for nearby medical institutions and provides a list of appropriate medical institutions.

[1566] 6. Digital storage and sharing of ultrasound images

[1567] Step 1:

[1568] The user scans the Echo photo and uploads it using the device, which then sends the uploaded photo to the server.

[1569] Step 2:

[1570] The server organizes the photos by date and displays them in an album format.

[1571] Step 3:

[1572] The user selects who (family, friends) they want to share the photos with, and the device sends the selection information to the server.

[1573] Step 4:

[1574] The server generates a shared link and notifies the specified recipient.

[1575] 7. Consultation function as a communication partner

[1576] Step 1:

[1577] The user inputs a question into the chatbot within the app, and the device sends the input data to the server.

[1578] Step 2:

[1579] The server uses an AI engine to analyze the question and find the appropriate answer.

[1580] Step 3:

[1581] The server notifies the user of the generated answer.

[1582] Step 4:

[1583] The user enters an additional question, and the device sends the input data to the server again.

[1584] Step 5:

[1585] The server reparses and generates successively appropriate answers.

[1586] Example 1

[1587] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1588] Families expecting their first pregnancy need information and support regarding pregnancy and childbirth, but there are limited ways to receive appropriate advice and support. In particular, important information such as measures to combat morning sickness and how to apply for subsidies needs to be tailored to each individual's situation. For this reason, a system that can provide efficient and comprehensive support is needed.

[1589] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1590] In this invention, the server includes a means for inputting data on the lifestyle patterns and health conditions of the subject, a means for transmitting the data from a terminal to the server, a server equipped with a data analysis means for generating support information related to pregnancy and childbirth based on the data, and a means for transmitting the support information from the server to the terminal and notifying the user. This makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[1591] "Subject's lifestyle patterns" refers to the totality of an individual's daily behaviors and habits, including meal times, sleep times, and daily activity status.

[1592] "Health status" refers to data that indicates an individual's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[1593] "Means for inputting data" refers to the means by which users input information about their lifestyle patterns and health status, such as using a dedicated application or web portal.

[1594] "Means for transmitting data from a terminal to a server" refers to a communication means for transmitting data from a terminal (e.g., smartphone, PC) to a server via the Internet.

[1595] "Data analysis means" refers to algorithms and software that analyze and process data in order to generate support information related to pregnancy and childbirth based on the received data.

[1596] "Support information" refers to a comprehensive range of information related to pregnancy and childbirth, such as measures to prevent morning sickness, health management advice, and how to apply for subsidies.

[1597] "Server" refers to a computer system for storing data, analyzing data, generating and transmitting support information.

[1598] "Terminal" refers to a device used by a user to input and receive data, such as a smartphone or computer.

[1599] "Means of notification" refers to methods such as push notification, email, or in-app notification for notifying the user of the generated support information.

[1600] The present invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. Specific embodiments are described below.

[1601] First, users access a dedicated application or web portal and enter data about their lifestyle and health conditions, including meal times, sleep time, daily activity levels, weight, blood pressure, and whether they experience morning sickness. This data is entered from the user's smartphone, computer, or other device.

[1602] Next, the terminal sends the input data to a server via the Internet. At this time, the data is sent safely using a secure communication method. The server receives the data and stores it in a database.

[1603] The server analyzes the received data using a generative AI model (e.g., GPT-3 or BERT) to generate personalized support information based on the user's lifestyle and health status. This support information covers a wide range of topics, including advice on morning sickness, health management, and how to apply for subsidies.

[1604] As a specific example, if a user inputs the time and content of their breakfast, such as "I had toast and orange juice at 7:30," the server will analyze that information and generate specific dietary advice to alleviate morning sickness, such as "I recommend having some easily digestible fruit and light protein for your next breakfast."

[1605] The generated support information is sent from the server to the user's device. Notifications can be sent in various ways, such as push notifications, emails, and in-app notifications. For example, advice on how to deal with morning sickness can be sent as a push notification, and when the user taps the notification to open the application, detailed advice will be displayed.

[1606] An example of a prompt sentence would be, "What should I do if I have severe morning sickness?" In this case, the server will analyze it using an AI engine and respond with specific advice such as, "Try to drink plenty of fluids and eat light meals."

[1607] This system makes it possible to provide a wide range of support information efficiently and in a timely manner to families expecting their first pregnancy.

[1608] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1609] Step 1:

[1610] Users access a dedicated application or web portal and enter their lifestyle patterns and health status. Data entered includes meal times, meal contents, sleep time, daily activity status, weight, blood pressure, and whether or not they have morning sickness. For example, users enter information such as "I had toast and orange juice for breakfast at 7:30." This information is saved on the device as initial data.

[1611] Step 2:

[1612] The device sends the entered data to the server. The data is sent via the Internet using a secure communication method. Specifically, the data sent includes the meal details and time entered by the user, and the information "I had toast and orange juice at 7:30" is transferred. The server receives this data and stores it in a database.

[1613] Step 3:

[1614] The server analyzes the received data. Based on the received data, a generative AI model (e.g., GPT-3 or BERT) is used to understand the user's lifestyle patterns and health status. Here, data from the past few weeks is statistically analyzed to identify trends such as "irregular breakfast times." This allows for a deeper understanding of the user's lifestyle patterns.

[1615] Step 4:

[1616] The server generates support information for pregnancy and childbirth based on the analysis results. Based on the lifestyle patterns and health condition data obtained from the analysis, the generative AI model generates appropriate advice and support information. For example, the generated advice might be, "We recommend that you eat easily digestible fruit and light protein for your next breakfast." The generated support information is temporarily stored on the server.

[1617] Step 5:

[1618] The server sends the generated support information to the user's device. Notification methods can be selected from a variety of methods, including push notifications, emails, and in-app notifications. A specific example of how this works is a notification such as, "To alleviate morning sickness, we recommend eating easily digestible fruit and light protein for breakfast."

[1619] Step 6:

[1620] The user receives a notification on their device and opens the app or web portal to view the support information. When the user taps the notification on their smartphone, detailed advice is displayed within the app, allowing the user to review the support information provided and incorporate it into their daily life.

[1621] Through the above processing steps, efficient and timely support information is provided to families expecting their first pregnancy.

[1622] (Application example 1)

[1623] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1624] Families expecting their first pregnancy need comprehensive support regarding pregnancy and childbirth, but current systems lack information tailored to each individual user. Furthermore, there is no mechanism in place to provide the necessary content in a timely manner, making it difficult for information recipients to receive the necessary support at the appropriate time. This creates a problem in which users are unable to feel at ease during their pregnancy.

[1625] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1626] In this invention, the server includes a means for receiving data on the lifestyle patterns and health conditions of a subject, a means for generating support information related to pregnancy and childbirth based on the data, a means for generating the support information as content suitable for each individual, and a means for delivering the content to the user, thereby enabling personalized content optimized for each individual user to be delivered in a timely manner.

[1627] "Covered Person" refers to an individual or family member who needs assistance with pregnancy and childbirth.

[1628] "Lifestyle patterns" refers to the subject's daily habits and behaviors, such as meal times, sleep times, and activity levels.

[1629] "Health status" refers to the subject's physical condition, such as weight, blood pressure, and whether or not they have morning sickness.

[1630] "Data receiving means" refers to a device or method for acquiring data on the subject's lifestyle patterns and health status and inputting it into the system.

[1631] "Support information generation means" refers to a device or method that generates appropriate advice and information regarding pregnancy and childbirth based on the received data.

[1632] The "content generation means" refers to a device or method for creating the generated support information as content in a form optimized for each individual target person.

[1633] "Content distribution means" refers to a device or method for distributing generated content to a target person in a timely manner.

[1634] "Morning sickness advice" refers to information that includes specific suggestions for dietary and lifestyle changes to reduce morning sickness symptoms.

[1635] "Subsidy application support information" refers to details of subsidies available to eligible persons and support information regarding the application procedures.

[1636] MODE FOR CARRYING OUT THE INVENTION

[1637] The present invention provides a system for providing comprehensive support for pregnancy and childbirth to a subject. Specific embodiments of the system are described below.

[1638] The system has the following main functions:

[1639] 1. Entering information about the subject and receiving data

[1640] Users enter data about their lifestyle and health status via a dedicated application or web portal, including meal times, sleep time, activity level, weight, blood pressure, and whether they suffer from morning sickness. The data entered by the user is sent to a server via the device, where it is stored in the system's central database.

[1641] As a specific example, when a user enters the time and content of breakfast into an application, the device sends the information to a server and stores it in a database.

[1642] 2. Data analysis and generation of supporting information

[1643] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes advice on how to deal with morning sickness, health management, and assistance with applying for subsidies. Furthermore, the support information is generated as personalized content and provided in the most appropriate format for each individual user.

[1644] As a specific example, the server analyzes the user's eating patterns and generates dietary advice to alleviate morning sickness. It also advises the user on the appropriate pace of weight gain based on their weight data.

[1645] 3. Sending and displaying support information

[1646] The server notifies the user of the generated assistance information and displays it through the application or web portal. Notifications can be made via push notifications, emails, or in-application notifications. The user can then use their device to review the received assistance information and adjust their actions accordingly.

[1647] For example, the server generates morning sickness advice and sends it to the user's smartphone as a push notification. When the user opens the application, they can view the advice.

[1648] Hardware and software used

[1649] Smartphone: Used as a device for entering information and receiving notifications.

[1650] Server: Used as a backend server for data analysis, support information generation, and notification distribution. For example, AWS or Google Cloud Platform can be used.

[1651] AI engines: Used for data analysis and content generation, specifically generative AI models such as TensorFlow and PyTorch.

[1652] Examples of specific examples and prompts

[1653] Below are examples of specific prompt sentences to be fed into the generative AI model.

[1654] Analyze the user's lifestyle and health data to generate recommended content for the day, such as dietary advice to alleviate morning sickness or exercise advice for weight management.

[1655] By using such prompts, the AI ​​engine can generate appropriate support information and provide it to the user in a personalized format, enabling the system to provide the necessary information at the appropriate time during pregnancy.

[1656] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1657] Step 1:

[1658] A user uses a dedicated application or web portal to input data about their lifestyle and health status (e.g., meal times, sleep time, activity level, weight, blood pressure, presence or absence of morning sickness). The input data is received by the device. The input in this step is data about the user's lifestyle and health status, and the output is data stored in the device.

[1659] Step 2:

[1660] The terminal sends the data entered by the user to the server. Specifically, the terminal sends the data to the specified endpoint via the network. The input is the data stored on the terminal, and the output is the data received by the server.

[1661] Step 3:

[1662] The server analyzes the data it receives. Specifically, the server uses an AI engine to generate support information for pregnancy and childbirth based on the user's lifestyle patterns and health status data. At this time, personalized content is generated by a generative AI model. The input is the data received by the server, and the output is the generated support information.

[1663] Step 4:

[1664] Based on the support information generated by the server, the server generates optimal content for each individual user. Specifically, content such as advice on how to deal with morning sickness and health management is created based on the results obtained from the AI ​​engine. The input is analyzed data, and the output is personalized content.

[1665] Step 5:

[1666] The server notifies the user of the generated content. Specifically, the server sends a push notification, email, or in-app notification to the device. The input is the generated content, and the output is the notification to the user.

[1667] Step 6:

[1668] The user receives a notification and opens an application or web portal to view supporting information. Specifically, the user reviews the content within the app and adjusts their behavior accordingly. The input is the received notification and the output is the user action.

[1669] In this way, the entire system works together to provide users with comprehensive and personalized support regarding pregnancy and childbirth.

[1670] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1671] This invention is a system that provides comprehensive support regarding pregnancy and childbirth to families expecting their first pregnancy, and by combining it with an emotion engine that recognizes the user's emotions, it can provide even more comprehensive support.

[1672] 1. Entering information about the subject and receiving data

[1673] The user uses a dedicated application or web portal to input their lifestyle patterns (e.g., meal times, sleep times, daily activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biometric sensor data, etc.). The device then sends this information to the server.

[1674] Examples:

[1675] 1. The user enters the time and contents of breakfast into the food diary within the application, and the emotion engine reports via voice input that they feel "tired."

[1676] 2. The device sends the input data and voice data to the server and stores them in a database.

[1677] 2. Data analysis and generation of supporting information

[1678] Based on the data received by the server, it generates support information related to pregnancy and childbirth. Support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[1679] Examples:

[1680] 1. The server analyzes the user's eating patterns and emotional data and generates dietary advice to alleviate morning sickness. If the user feels "fatigue," the server generates advice that takes this into account.

[1681] 2. The server advises the user on the appropriate pace of weight gain based on the weight and emotional data entered by the user. If the user feels "anxious," it also suggests ways to relax.

[1682] 3. Sending and displaying support information

[1683] The server generates and notifies the user of the assistance information and displays it through the application or web portal, using methods such as push notifications, emails, or in-app notifications.

[1684] Examples:

[1685] 1. The server generates morning sickness prevention advice and sends it to the user's smartphone as a push notification. If the user feels "fatigue," it also sends a notification encouraging them to take a rest.

[1686] 2. The user opens the application and views the provided advice.

[1687] More morning sickness advice

[1688] The server and emotion engine work together to analyze the user's lifestyle patterns and emotional data, and provide advice on dietary and lifestyle changes to alleviate morning sickness.

[1689] Examples:

[1690] 1. The user inputs the times and contents of meals they ate each day, as well as the emotions they felt.

[1691] 2. Based on this information, the server and emotion engine will recommend a lighter meal for the next meal and, if the user feels "tired," will also suggest taking time to rest.

[1692] Subsidy application support details

[1693] The server and emotion engine work together to provide applicable grant information and guide the application process based on the user's situation and emotion data. It also provides a checklist of required documents and deadlines for submission. If the user feels anxious or stressed, it also provides psychological support information.

[1694] Examples:

[1695] 1. The user enters their address, how many weeks pregnant they are, and how they feel.

[1696] 2. The server and emotion engine search for local subsidy information and guide users on how to apply for the appropriate subsidy, while also providing psychological support information if the user feels "anxious."

[1697] Health Management Details

[1698] The server analyzes weight and blood pressure data entered periodically and issues a warning if the data falls outside the normal range. Furthermore, the emotion engine analyzes the emotional data and suggests appropriate relaxation methods or visiting a medical institution if the user is feeling anxious or stressed.

[1699] Examples:

[1700] 1. The user enters their daily weight, blood pressure, and emotional data.

[1701] 2. The server and emotion engine detect abnormal values, notify the user to seek medical attention immediately, and suggest relaxation methods.

[1702] Learn more about storing and sharing ultrasound photos

[1703] Users upload ultrasound images, which are then stored on a server in chronological order, and users can easily share them with family and friends if they wish.

[1704] Examples:

[1705] 1. The user uploads their most recent ultrasound photo to the app.

[1706] 2. The server organizes the photos and generates links for family members to share them.

[1707] Details of the consultation function as a communication partner

[1708] Users can input their pregnancy-related questions and concerns into the chatbot within the app, and the server will use AI and an emotion engine to provide appropriate advice and information, thereby providing even greater support.

[1709] Examples:

[1710] 1. When users ask questions in the app such as "What should I do if I have severe morning sickness?", they also input the emotions they are feeling.

[1711] 2. The server and emotion engine analyze the question and respond with advice such as "Make sure to drink plenty of water and eat light meals," and if the user is "worried," it also suggests further ways to relax.

[1712] The above is a detailed embodiment for carrying out the present invention. By utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be realized for families experiencing their first pregnancy.

[1713] The processing flow will be explained below.

[1714] Processing flow of a system that combines emotion engines

[1715] 1. Information input and data reception

[1716] Step 1:

[1717] Users enter their lifestyle patterns (e.g., meal times, sleep times, daily activity status) and health conditions (e.g., weight, blood pressure, presence or absence of morning sickness) into a dedicated application or web portal, along with emotional data (e.g., text input, voice input, biometric sensor data, etc.).

[1718] Step 2:

[1719] The terminal transmits the information entered by the user to the server.

[1720] Step 3:

[1721] The server stores the received information in a database, and sends the emotion data to the emotion engine.

[1722] 2. Data analysis and generation of supporting information

[1723] Step 1:

[1724] The server analyzes the stored lifestyle patterns, health status, and emotional data.

[1725] Step 2:

[1726] The emotion engine analyzes the emotion data and identifies the user's current emotional state (e.g., "tired," "anxious," "stressed").

[1727] Step 3:

[1728] The server generates support information for pregnancy and childbirth based on the analysis results, and also reflects the analysis results of the emotion engine.

[1729] 3. Sending and displaying support information

[1730] Step 1:

[1731] The server notifies the user of the assistance information generated by the server via push notification, email, or in-app notification.

[1732] Step 2:

[1733] The user opens the application and views the notified support information.

[1734] Specific function processing flow

[1735] Morning sickness prevention advice

[1736] Step 1:

[1737] Users enter the time and content of their meals throughout the day, as well as the emotions they are feeling, into the app.

[1738] Step 2:

[1739] The device sends input data and emotion data to the server.

[1740] Step 3:

[1741] The server analyzes the data and generates dietary advice to alleviate morning sickness.

[1742] Step 4:

[1743] The emotion engine analyzes the emotional data and, if the user is feeling "tired," generates advice that takes that state into account.

[1744] Step 5:

[1745] The server notifies the user of the generated advice.

[1746] Subsidy application support

[1747] Step 1:

[1748] The user enters their address, how many weeks pregnant they are, and how they are feeling.

[1749] Step 2:

[1750] The terminal transmits the input data and emotion data to the server.

[1751] Step 3:

[1752] The server searches for local grant information and generates notifications guiding users on how to apply for the appropriate grant.

[1753] Step 4:

[1754] The emotion engine analyzes the user's emotional data, and if the user is feeling "anxiety," it also generates mental support information that takes that state into account.

[1755] Step 5:

[1756] The server generates grant application guides and provides moral support information to the user.

[1757] Health management data analysis and outlier detection

[1758] Step 1:

[1759] Users periodically enter their weight, blood pressure, and emotional data into the app.

[1760] Step 2:

[1761] The terminal sends the input data to the server.

[1762] Step 3:

[1763] The server analyzes the data and generates alerts if outliers are detected.

[1764] Step 4:

[1765] The emotion engine analyzes the emotional data and, if the user is feeling "worried" or "stressed," suggests relaxation methods or visiting a medical institution that take that state into consideration.

[1766] Step 5:

[1767] The server will warn the user about the abnormal value and inform them how to relax.

[1768] Storage and sharing of ultrasound images

[1769] Step 1:

[1770] The user scans the ultrasound photo and uploads it using the device.

[1771] Step 2:

[1772] The device sends the uploaded photos to the server.

[1773] Step 3:

[1774] The server organizes the photos by date and displays them in an album format.

[1775] Step 4:

[1776] The user selects who (family, friends) they want to share the photos with.

[1777] Step 5:

[1778] The terminal transmits the selection information to the server.

[1779] Step 6:

[1780] The server generates a shared link and notifies the specified recipient.

[1781] Consultation function as a communication partner

[1782] Step 1:

[1783] Users input questions into the in-app chatbot, along with their emotional state.

[1784] Step 2:

[1785] The terminal sends the input data to the server.

[1786] Step 3:

[1787] The server uses an AI engine and an emotion engine to analyze the question and emotion data and search for the appropriate answer.

[1788] Step 4:

[1789] The server notifies the user of the generated answer, along with additional advice based on the user's emotional state.

[1790] Step 5:

[1791] The user enters a follow-up question.

[1792] Step 6:

[1793] The terminal again sends the input data to the server.

[1794] Step 7:

[1795] The server re-parses and subsequently generates the appropriate answer and notifies the user.

[1796] The above is a specific implementation of a system that combines an emotion engine. This processing flow allows users to receive more personalized support for pregnancy and childbirth.

[1797] Example 2

[1798] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1799] The challenge is to provide comprehensive support for first-time pregnant families regarding pregnancy and childbirth while providing personalized advice tailored to each user's emotional state. There is also a need to effectively utilize the information users input daily to provide the necessary support in real time.

[1800] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1801] In this invention, the server includes means for receiving data on the subject's lifestyle patterns, health status, and emotions, means for generating support information related to pregnancy and childbirth based on the data and for analyzing the emotion data to complement and optimize the support information, means for transmitting the support information and displaying it through an application or web portal, means for organizing and storing image data uploaded by users in chronological order, and means for users to input questions and provide appropriate advice using artificial intelligence and emotion analysis technology, thereby enabling users to efficiently receive a variety of support information tailored to their individual situations and emotions.

[1802] "Target individuals" are those who use this system to receive support regarding pregnancy and childbirth.

[1803] "Lifestyle patterns" refers to data such as meal times, sleep times, and daily activity status in the subject's daily life.

[1804] "Health status" refers to data related to the subject's health, such as weight, blood pressure, and whether or not they have morning sickness.

[1805] "Emotion data" refers to data related to emotions obtained from text, voice, biometric sensor data, etc. input by the subject.

[1806] A "server" is a computer system that processes received data and generates and transmits assistance information.

[1807] The "emotion engine" is a technology that analyzes emotional data and uses the results to complement and optimize support information.

[1808] "Support information" includes advice on pregnancy and childbirth, measures to prevent morning sickness, health management suggestions, and procedures for applying for subsidies.

[1809] An "application" is software used by a subject to enter information and receive assistance information.

[1810] A "web portal" is a website where subjects can enter information via a browser and receive support information.

[1811] "Image data" refers to image files uploaded by the subject, such as ultrasound images.

[1812] "Artificial intelligence" refers to technologies such as machine learning and natural language processing that analyze data and provide appropriate advice.

[1813] This invention is a system that provides comprehensive support for families expecting their first pregnancy regarding pregnancy and childbirth. In particular, the quality of support is improved by combining it with an emotion engine that recognizes the user's emotions.

[1814] First, the user uses a dedicated application or web portal to input their lifestyle patterns (meal times, sleep times, daily activity status), health status (weight, blood pressure, presence or absence of morning sickness), and emotional data (text input, voice input, biosensor data, etc.). The terminals used here are mobile devices such as smartphones and tablets.

[1815] The device then sends this information to the server, which uses the HTTPS protocol to transfer data securely. The server then stores the received data in a MariaDB database.

[1816] The server analyzes the received data and generates support information related to pregnancy and childbirth. This support information includes information on morning sickness, health management advice, and how to apply for subsidies. Technically, Python scripts are used for data analysis to generate the support information. The emotion engine also uses a generative AI model (e.g., the BERT model) to analyze the user's emotion data. Here, the emotion engine converts the voice data into text and extracts the user's emotion.

[1817] The generated support information is notified to the user by the server via push notification, email, or in-app notification, which is displayed on the user's smartphone or tablet.

[1818] To give a specific example, the user can input the time and contents of breakfast and how "tired" they feel by voice. The device sends this to the server, and the data is saved in a database. Based on the received data, the server generates dietary advice to alleviate morning sickness, reflecting the analysis results of the emotion engine and providing the user with advice such as "We recommend a light breakfast and ensure you get plenty of rest."

[1819] Users can also upload ultrasound images, which the server receives and stores in a database organized by date. If desired, a link can be generated to share the images with family and friends.

[1820] Furthermore, when questions or concerns about pregnancy are entered into the chatbot within the app, the server uses AI and an emotion engine to provide appropriate advice and information. For example, in response to the question, "What should I do if I have severe morning sickness?", the server will respond with, "Make sure to drink plenty of water and eat light meals." If the user is "worried," it will also suggest ways to relax.

[1821] An example of a prompt is as follows:

[1822] "I had bread and eggs at eight o'clock. I'm very tired now."

[1823] "What should I do if I have severe morning sickness?"

[1824] In this way, by utilizing the emotion engine, support for users can be more personalized, and comprehensive support can be provided to families experiencing their first pregnancy.

[1825] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1826] Step 1:

[1827] The user launches a dedicated application or web portal and enters data on their lifestyle, health status, and emotions.

[1828] Specific actions: The user opens the app on their smartphone, taps the "Meal Log" button, and then voice-records that they ate bread and eggs for breakfast at 8 a.m. and feel "tired."

[1829] Input: Lifestyle patterns (e.g., meal times, sleep times), health status (e.g., weight, blood pressure), emotional data (text input, voice input)

[1830] Output: The input data is saved on the device.

[1831] Step 2:

[1832] The terminal transmits the input data to the server.

[1833] Specific operation: The terminal converts the input information into packets in real time and sends them securely to the server using the HTTPS protocol.

[1834] Input: User-entered lifestyle patterns, health status, and emotional data

[1835] Output: The data is sent to the server.

[1836] Step 3:

[1837] The server stores the received data in a database.

[1838] Specific operation: The server receives the received information and stores it in the MariaDB database system.

[1839] Input: Data sent from the terminal

[1840] Output: The data is saved to a database.

[1841] Step 4:

[1842] The server analyzes the received data and generates support information, including information on morning sickness, health management advice, and how to apply for subsidies.

[1843] How it works: The server uses a Python script to analyze the received dietary and emotional data and generate dietary advice to alleviate morning sickness. The emotion engine uses the BERT model to convert the audio data into text and extract the emotion "fatigue." Based on this information, it recommends a light breakfast and encourages the user to get some rest.

[1844] Input: User data stored in the database

[1845] Output: Generated support information

[1846] Step 5:

[1847] The server notifies the user of the generated support information.

[1848] Specific operation: The server prepares the generated dietary advice and rest notification and sends it as a push notification. The device receives the notification and displays a message such as "To alleviate morning sickness, we recommend having a light breakfast next time" in the notification bar of the user's smartphone.

[1849] Input: Generated support information

[1850] Output: Sent to the user as a notification.

[1851] Step 6:

[1852] The user receives the notified advice.

[1853] What happens: The user taps on the notification on their smartphone and sees detailed advice in the app.

[1854] Input: Notified support information

[1855] Output: The user views and acts on the advice.

[1856] Step 7:

[1857] Users upload ultrasound photos, which are then stored on a server in chronological order, and if desired, a link is generated to share with family and friends.

[1858] Specific operation: The user taps the "Upload Echo Photo" button, selects and uploads a photo. The server receives the photo, organizes it by date, and stores it in a database. If desired, a sharing link is generated and notified to the user.

[1859] Input: Ultrasound image data

[1860] Output: Organized photo data and a shareable link

[1861] Step 8:

[1862] Users input their questions and concerns into the in-app chatbot, and the server uses AI and an emotion engine to provide appropriate advice.

[1863] Specific operation: When a user asks a question in the app, such as "What should I do if I have severe morning sickness?", they also input their emotions. The server and emotion engine analyze the question and emotion data, and respond with advice such as "Make sure to drink plenty of water and eat light meals." If the user is "worried," the app also suggests further relaxation methods.

[1864] Input: Question content and emotion data

[1865] Output: Good advice

[1866] (Application example 2)

[1867] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1868] Families expecting their first child often need a lot of information about pregnancy and childbirth, and in particular emotional support. However, conventional support systems lack the ability to provide support information that takes into account each individual's emotional state, and provide intuitive information acquisition methods using virtual environments, which means they are unable to fully alleviate users' anxiety and stress.

[1869] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving data on the subject's lifestyle patterns and health condition, means for generating support information related to pregnancy and childbirth based on the data, means for analyzing emotion data and complementing and optimizing the support information, means for displaying the generated support information in a virtual environment, and means for transmitting the support information. This makes it possible for the user to intuitively obtain personalized support information in a virtual environment that takes into account the user's emotional state, thereby reducing anxiety and stress.

[1870] "Target" refers to users who use this system to receive support regarding pregnancy and childbirth.

[1871] "Lifestyle patterns" refers to the subject's daily habits and behaviors, specifically including meal times, sleep times, daily activity status, etc.

[1872] "Health condition" is information that indicates the physical condition of the subject, and specifically includes weight, blood pressure, whether or not the subject has morning sickness, and the like.

[1873] "Emotion data" is information that indicates the emotional state of a subject, and specifically includes text input, voice input, biometric sensor data, and the like.

[1874] "Support information" refers to advice and guidance regarding pregnancy and childbirth that is generated based on the subject's lifestyle patterns and health status.

[1875] An "emotion recognition engine" is software that analyzes input emotional data and recognizes the emotional state of the subject.

[1876] A "virtual environment" is a virtual reality space that subjects can experience using a head-mounted display or smartphone.

[1877] A "virtual display" is a means of displaying information in a virtual reality space, allowing the subject to intuitively obtain the information.

[1878] "Complement and optimization" refers to using the analyzed emotional data to adjust the support information to the emotional state of the target person and provide it in the most optimal form.

[1879] A "generative AI model" is a model that generates information using artificial intelligence (AI), specifically performing natural language processing and data analysis.

[1880] This invention is a system that allows a subject to receive comprehensive support regarding pregnancy and childbirth, and improves the quality of support by combining an emotion engine. The following describes in detail the mode for carrying out the invention.

[1881] 1. Data Entry and Emotion Recognition

[1882] Users use a smartphone or head-mounted display to input data on their daily life patterns (e.g., meal times, sleep time, activity status) and health status (e.g., weight, blood pressure, presence or absence of morning sickness). Emotional data can also be input using text input, voice input, biosensor data, etc. The device then transmits this data to the server in real time.

[1883] A specific example of its use is when a user enters the time and content of breakfast into the application's food diary, and then reports by voice that they feel "tired" using the emotion engine. This data is sent from the device to the server and stored in a database.

[1884] 2. Data analysis and generation of supporting information

[1885] The server generates support information for pregnancy and childbirth based on the received data. The support information includes measures to prevent morning sickness, health management advice, and how to apply for subsidies. In addition, an emotion engine analyzes the user's emotional data and complements and optimizes the support information based on the results.

[1886] 3. Sending support information and displaying it in the virtual environment

[1887] The support information generated by the server is displayed intuitively to the user in the virtual environment. The user can efficiently receive the support information via a head-mounted display or smartphone. This information can also be sent via push notifications or email.

[1888] Specific examples

[1889] 1. The user inputs the time and content of meals they had eaten that day, as well as the emotions they felt. Based on this information, the server and emotion engine recommend a lighter meal for the next meal, and if the user feels "tired," it also suggests taking time to rest.

[1890] 2. The user enters their address, number of weeks pregnant, and their feelings. The server and emotion engine search for local subsidy information and guides them on how to apply for the appropriate subsidy. If the user feels anxious, they will also be provided with information on psychological support.

[1891] Prompt Sentence Examples

[1892] "When a user asks 'What to do if morning sickness is severe,' we know that the user is feeling 'worried.' Please provide appropriate advice and suggest ways to relax."

[1893] This allows support information to be generated that takes into account the user's emotional state, enabling intuitive information acquisition in a virtual environment.

[1894] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1895] Step 1:

[1896] The user uses a smartphone or head-mounted display to input lifestyle patterns (e.g., meal times, sleep times, activity status), health conditions (e.g., weight, blood pressure, presence or absence of morning sickness), and emotional data (e.g., text input, voice input, biosensor data). This data is sent to the server in real time via the application.

[1897] Input: Life patterns, health status, emotional data

[1898] Output: User data sent to the server

[1899] Specific operation: The user enters various data within the app and presses the send button.

[1900] Step 2:

[1901] The server stores the received data in a database and prepares it for analysis. The stored data is categorized into lifestyle patterns, health status, and emotional data.

[1902] Input: Various data sent by the user

[1903] Output: Classified user data

[1904] Specific behavior: Inserts received data into a database and organizes it by category.

[1905] Step 3:

[1906] The server analyzes the stored data and generates support information for users regarding pregnancy and childbirth, including information on how to deal with morning sickness, health management advice, and how to apply for subsidies.

[1907] Input: Classified user data

[1908] Output: Generated support information

[1909] Specific operation: Executes the analysis algorithm and generates appropriate support information.

[1910] Step 4:

[1911] The server uses an emotion engine to analyze the user's emotional data and complements and optimizes the support information based on the results. For example, if the user feels "fatigue," it will suggest taking time to rest.

[1912] Input: User emotion data and assistance information generated in the previous step

[1913] Output: Optimized support information

[1914] Specific operation: Emotion data is input into the emotion engine and reflected in the support information.

[1915] Step 5:

[1916] The server sends the generated and optimized assistance information to a head-mounted display or smartphone for display in the virtual environment, and also notifies the user of the information via push notification or email as needed.

[1917] Input: Optimized support information

[1918] Output: Assistance information displayed on the user's device

[1919] Specific behavior: Sends assistance information to the appropriate device and converts it into a format for display.

[1920] Step 6:

[1921] Users can view support information in a virtual environment and use it to manage their health and take various steps related to pregnancy, such as checking dietary tips to combat morning sickness or specific procedures for applying for subsidies.

[1922] Input: Assistive information displayed on the device

[1923] Output: User actions and decisions

[1924] Specific actions: Plan and execute actions based on support information.

[1925] The above processing steps provide personalized support information that takes into account the user's emotional state, and enable intuitive acquisition in a virtual environment.

[1926] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1927] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1928] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1929] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1930] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1931] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1932] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1933] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1934] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1935] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1936] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1937] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1938] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1939] 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.

[1940] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1941] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1942] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1943] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1944] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1945] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1946] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1947] The following is further disclosed regarding the above embodiment.

[1948] (Claim 1)

[1949] means for receiving data on the subject's lifestyle patterns and health status;

[1950] A means for generating support information regarding pregnancy and childbirth based on the data;

[1951] means for transmitting the support information;

[1952] A system including:

[1953] (Claim 2)

[1954] 2. The system according to claim 1, wherein the support information includes advice on how to deal with morning sickness.

[1955] (Claim 3)

[1956] 2. The system according to claim 1, wherein the support information includes support information relating to procedures for applying for subsidies.

[1957] "Example 1"

[1958] (Claim 1)

[1959] a means for inputting data on the subject's lifestyle and health status;

[1960] means for transmitting the data from the terminal to a server;

[1961] a server equipped with data analysis means for generating support information related to pregnancy and childbirth based on the data;

[1962] means for transmitting the support information from the server to the terminal and notifying the user;

[1963] A system including:

[1964] (Claim 2)

[1965] 2. The system according to claim 1, wherein the support information includes advice on how to deal with morning sickness.

[1966] (Claim 3)

[1967] 2. The system according to claim 1, wherein the support information includes support information relating to procedures for applying for subsidies.

[1968] "Application Example 1"

[1969] (Claim 1)

[1970] means for receiving data on the subject's lifestyle patterns and health status;

[1971] A means for generating support information regarding pregnancy and childbirth based on the data;

[1972] means for generating the support information as individually suitable content;

[1973] means for delivering said content to users;

[1974] A system including:

[1975] (Claim 2)

[1976] 2. The system according to claim 1, wherein the support information includes advice on how to deal with morning sickness.

[1977] (Claim 3)

[1978] 2. The system according to claim 1, wherein the support information includes support information relating to procedures for applying for subsidies.

[1979] "Example 2: Combining Emotion Engines"

[1980] (Claim 1)

[1981] means for receiving the subject's life pattern, health status, and emotion data;

[1982] A means for generating support information for pregnancy and childbirth based on the data, and for analyzing emotion data to complement and optimize the support information;

[1983] means for transmitting and displaying said assistance information through an application or web portal;

[1984] A means for organizing and storing image data uploaded by users in chronological order;

[1985] A means for users to input questions and receive appropriate advice using artificial intelligence and sentiment analysis technology;

[1986] A system including:

[1987] (Claim 2)

[1988] 2. The system according to claim 1, wherein the support information includes advice on how to deal with morning sickness.

[1989] (Claim 3)

[1990] 2. The system according to claim 1, wherein the support information includes support information relating to procedures for applying for subsidies.

[1991] "Application example 2 when combining emotion engines"

[1992] (Claim 1)

[1993] means for receiving data on the subject's lifestyle patterns and health status;

[1994] A means for generating support information regarding pregnancy and childbirth based on the data;

[1995] A means for analyzing emotion data and complementing and optimizing assistance information;

[1996] a means for displaying the generated support information in a virtual environment;

[1997] means for transmitting the support information;

[1998] A system including:

[1999] (Claim 2)

[2000] 2. The system according to claim 1, wherein the support information includes advice on how to deal with morning sickness and is displayed using a virtual display.

[2001] (Claim 3)

[2002] 2. The system according to claim 1, wherein the support information includes support information relating to procedures for applying for subsidies, and also suggests relaxation methods based on emotion data. [Explanation of symbols]

[2003] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving data on the subject's lifestyle patterns and health status; A means for generating support information related to pregnancy and childbirth based on the data; means for transmitting the support information; A system including:

2. The system according to claim 1 , wherein the support information includes advice on how to deal with morning sickness.

3. 2. The system according to claim 1, wherein the support information includes support information relating to procedures for applying for a subsidy.

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