system

Abstract

We provide the system. [Solution] A sensor means for continuously collecting physiological data of pregnant women, A transmission means for transmitting data collected from the aforementioned sensor means via a communication network, A server means for receiving and analyzing data transmitted from the aforementioned transmission means, A means for generating individual nutrition and exercise advice tailored to the health condition of pregnant women, A notification means for notifying the user of the advice generated by the generation means, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] [[ID=2​​​​​​​​​​​​​​​​​​​This invention proposes a device equipped with sensors for continuously collecting physiological data on pregnant women and having the function of transmitting this data through a communication network. The server that receives the data analyzes it and generates nutrition and exercise advice tailored to each pregnant woman. The generated advice is immediately notified to the user, and the device also has an AI function that provides optimal answers to inquiries from pregnant women. Furthermore, by acquiring the latest medical information and reflecting it in the generated advice, it provides more accurate support. In this way, this invention alleviates the anxieties faced by pregnant women and supports a healthy pregnancy.

[0006] "Sensor means" refers to a device or function for continuously measuring and collecting physiological data of pregnant women.

[0007] "Transmission means" refers to a device or function for transmitting data collected by sensor means to a server via a communication network.

[0008] "Server means" refers to a computer or system that analyzes data received from the transmission means and evaluates the health status of pregnant women.

[0009] The "generation means" refers to a function that generates nutritional and exercise advice tailored to each pregnant woman from the information analyzed by the server means.

[0010] "Notification means" refers to a device or function that conveys advice created by the generation means to the user.

[0011] "AI means" refers to a function that uses artificial intelligence technology to generate appropriate answers to inquiries from pregnant women.

[0012] "Update mechanism" refers to a function for obtaining the latest medical information and updating the data on the server. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. [[ID=十七]]

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The present invention is a system in which sensor means, transmission means, server means, generation means, notification means, AI means, and update means work in cooperation with each other to provide individualized health support for pregnant women. The operation of each component is described below.

[0035] Sensor means and transmission means:

[0036] A wearable device worn by the pregnant woman functions as a sensor, continuously collecting physiological data such as heart rate and sleep patterns. This data is transmitted to a server via a transmission device at regular intervals using a secure connection.

[0037] Server means and generation means:

[0038] The server receives and analyzes data sent from the transmission unit. The analysis is performed by an AI algorithm to assess the health status of pregnant women. Based on the evaluated data, the generation unit generates nutritional and exercise advice tailored to the individual health status of each pregnant woman. The generated advice takes into account the progress of the pregnancy and past data.

[0039] Means of notification:

[0040] The generated advice is notified to the user in real time via a notification system. It is delivered as a push notification to the user's smartphone or tablet, allowing the user to receive the information at the appropriate time.

[0041] AI means:

[0042] Users can input questions and concerns via text or voice through the application. The AI ​​system uses the input information to generate the best possible answer based on an FAQ database and past cases, and then provides this answer as feedback to the user.

[0043] Update method:

[0044] The server system uses an API for medical collaboration to periodically retrieve the latest medical information and update the database. This ensures that the advice provided by the generation system is always based on the most up-to-date information.

[0045] Specific example:

[0046] For example, suppose a pregnant woman feels mild nausea around noon.

[0047] Terminal: The wearable device sends its current heart rate and activity level to the server.

[0048] Server: Analyzes the data and confirms a slight increase in body temperature.

[0049] Generation method: Generate specific advice recommending hydration and rest to the user.

[0050] Notification method: Advice is delivered immediately to the user's device.

[0051] User: I followed the recommendations and my symptoms improved the next day.

[0052] This invention will allow pregnant women to manage their health with greater peace of mind and support them in having an ideal pregnancy.

[0053] The following describes the processing flow.

[0054] Step 1:

[0055] The device collects physiological data from pregnant women. Specifically, it measures heart rate, activity level, sleep patterns, etc., using sensors in the wearable device and records the data at regular intervals.

[0056] Step 2:

[0057] The device transmits recorded physiological data. It then initiates a process to securely transmit the data to a server via wireless communication such as Wi-Fi or Bluetooth.

[0058] Step 3:

[0059] The server receives data sent from the terminal. It then passes the received data to the analysis engine and prepares it for analysis.

[0060] Step 4:

[0061] The server analyzes the received data using an AI model. During this process, it evaluates for any physiological changes that deviate from the normal range, thereby monitoring the user's health status.

[0062] Step 5:

[0063] The server generates nutrition and exercise advice based on the analysis results. It devises personalized advice tailored to the pregnant woman's health condition and past data.

[0064] Step 6:

[0065] The server sends the generated advice to the terminal using a notification method. It prepares to deliver the advice as a push notification to the user's smart device.

[0066] Step 7:

[0067] The device notifies the user of advice. Push notifications display the advice on the user's smartphone or tablet, allowing the user to check it immediately.

[0068] Step 8:

[0069] Users enter questions and feedback using the application. They send their questions to the system via text or voice through the application's interface.

[0070] Step 9:

[0071] The server generates answers using AI based on user questions. It utilizes FAQ data and past case studies to construct the optimal response.

[0072] Step 10:

[0073] The server sends the generated response to the terminal and notifies the user. The user can then check the response within the application, gain reassurance, and obtain information to resolve their questions.

[0074] (Example 1)

[0075] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0076] Pregnant women experience various physiological changes during pregnancy, and appropriate health management is required in response to these changes. However, it is difficult to continuously provide appropriate advice based on individual health information. Furthermore, there are limited means to quickly and accurately answer the anxieties and questions that pregnant women have. In addition, it is necessary to effectively incorporate the latest medical information. There is a need to provide a system that can solve these problems and support pregnant women in having a safe and secure pregnancy.

[0077] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0078] In this invention, the server includes a detection means for collecting information on the physiological processes of pregnant women, a transmission means for communicating the information from the detection means, and an information processing means for receiving the information communicated from the transmission means and performing analysis using an artificial intelligence algorithm. This enables the provision of appropriate nutrition and exercise advice based on the individual health condition of pregnant women, prompt answers to questions, and the incorporation of the latest medical information.

[0079] "Detection means" refers to devices or technologies that continuously sense and collect information about a pregnant woman's menstrual cycle.

[0080] "Transmission means" refers to a device or technology for transmitting information obtained by the detection means to other devices or servers using an appropriate communication protocol.

[0081] "Information processing means" refers to devices and algorithms used to analyze received information, and in particular, those that utilize artificial intelligence technology to evaluate information and generate analysis results.

[0082] "Generation means" refers to a device or technology that generates nutritional and exercise guidelines suitable for the health condition of pregnant women based on the results analyzed by information processing means, and utilizes a generation AI model.

[0083] "Notification means" refers to devices or services used to provide pregnant women and their related parties with guidelines created by generation means, and which have the function of notifying information terminals.

[0084] "Artificial intelligence means" refers to a system or device that uses AI technology, including natural language processing technology, to generate appropriate responses to user questions and requests.

[0085] "Information update methods" refer to processes and technologies for obtaining the latest medical information from external sources and updating data within a system based on that information.

[0086] This invention is a system that provides individualized support for the health management of pregnant women, and operates by coordinating sensor means, transmission means, server means, generation means, notification means, artificial intelligence means, and information update means.

[0087] Sensor means and transmission means:

[0088] The device (wearable device) has the function of acquiring physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns. This data is continuously collected by sensors installed on the device. The collected data is then securely encrypted and transmitted to the server via a communication protocol using a transmission method.

[0089] Server means and generation means:

[0090] The server receives the transmitted data, stores it in a database, and analyzes it using artificial intelligence algorithms. Machine learning models (e.g., deep learning networks) are used in the analysis. Based on the analysis results, the server generates individualized nutrition and exercise guidelines tailored to the pregnant woman's health condition. The generation method utilizes a generative AI model to generate information based on the latest medical guidelines.

[0091] Means of notification:

[0092] The generated health guidelines are transmitted in real time to the user's smart device via a notification system. Through this notification, the user can quickly receive appropriate health information.

[0093] Artificial intelligence tools:

[0094] Users can input questions and concerns through the application. The server analyzes the input information and uses natural language processing technology to generate and provide feedback the most appropriate answers from an FAQ database and past cases. For example, a prompt sentence for the generating AI model might be, "Please tell me about safe exercise during pregnancy."

[0095] Information update method:

[0096] The server periodically retrieves the latest medical information using a medical collaboration API and updates the system's data based on that information. This ensures that the information provided to users is always up-to-date.

[0097] A concrete example would be a scenario where, if a pregnant woman experiences mild nausea during the day, the device sends her heart rate and activity level to a server. The server then analyzes the information, generates appropriate guidance, and sends the user advice recommending rest and fluid intake. In this way, pregnant women can accurately understand their health status and confidently take appropriate action.

[0098] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0099] Step 1:

[0100] The device (wearable device) continuously collects physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns using sensors. This information is temporarily stored in digital format within the device. Physiological information is acquired as input, and an encrypted data package is generated as output.

[0101] Step 2:

[0102] The terminal transmits collected physiological information to the server via a secure communication protocol (e.g., HTTPS). Data integrity checks are performed during transmission. Encrypted data packages are used as input, and the output is the data after transmission to the server is complete.

[0103] Step 3:

[0104] The server receives data sent from the terminal and checks its security. Once the data's integrity and completeness are confirmed, it is stored in the database. The input is physiological information received from the terminal, and the output is stored in the database.

[0105] Step 4:

[0106] The server analyzes the stored data using machine learning algorithms (e.g., deep learning) to assess the health status of pregnant women. Physiological information obtained from a database is used as input, and the analysis results are output.

[0107] Step 5:

[0108] The server (generation mechanism) uses a generation AI model based on the analysis results to generate individual nutrition and exercise guidelines for pregnant women. The latest medical guidelines are also taken into consideration. The input is the analysis results, and the output is health guidelines.

[0109] Step 6:

[0110] The server (notification mechanism) sends the generated health guidelines as a push notification to the user's smart device. The input is the generated health guidelines, and the output is the notification received on the user's device.

[0111] Step 7:

[0112] Users receive information through the application and, if necessary, input further questions or concerns via text or voice. The input consists of the user's responses or questions, and the output is feedback to the user interface.

[0113] Step 8:

[0114] The server (AI) analyzes the user's question and generates the optimal answer using natural language processing technology. The prompt used is "Use the generative AI model to generate an answer to the pregnant woman's question, 'What kind of exercise is safe during pregnancy?'" The input is the user's question, and the output is the generated answer, which is then fed back to the user.

[0115] Step 9:

[0116] The server (update mechanism) uses a medical information API to retrieve the latest medical information and update the database. The input is medical information obtained from an external source, and the output is the updated database.

[0117] (Application Example 1)

[0118] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0119] Providing timely and optimal advice and suggestions tailored to each pregnant woman's health condition is challenging. Furthermore, creating an environment where pregnant women can receive immediate and helpful product and service recommendations when they visit a store is also difficult.

[0120] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0121] In this invention, the server includes a sensor means, a transmission means, and a generation means. This makes it possible to continuously collect physiological data of pregnant women, generate personalized nutrition and exercise advice based on that data, and suggest products or services suitable for customers.

[0122] "Sensing means" refers to devices or equipment that continuously collect physiological data from pregnant women.

[0123] "Transmission means" refers to a device or function that has the ability to transmit data acquired by a sensor to a server via a communication network.

[0124] "Server means" refers to a computing device or system that analyzes data received from the transmission means and evaluates the health status of pregnant women.

[0125] "Generation means" refers to the process or device that creates individualized nutrition and exercise advice tailored to the health condition of pregnant women based on the analyzed data.

[0126] "Notification means" refers to communication devices or functions for transmitting advice created by the generation means to the user in real time.

[0127] "Display means" refers to a visible output device or function that suggests suitable products or services based on the health data of the customer who visits the store.

[0128] The system that implements this application includes a sensor that works in conjunction with a wearable device worn by a pregnant woman to continuously collect physiological data. A server receives and analyzes the data transmitted from this sensor. This analysis includes a process of evaluating the pregnant woman's health status using an AI algorithm. Based on the evaluation results, the server generates nutrition and exercise advice tailored to the pregnant woman's individual health condition. This generated advice is delivered in real time to the pregnant woman's smartphone via a notification system.

[0129] Furthermore, the server is equipped with a display system that uses in-store displays to recommend appropriate products or services based on the real-time health data of pregnant women who visit the store. This allows pregnant women to receive product suggestions tailored to their health condition when they visit the store.

[0130] For example, if a pregnant woman visits a store and notices a slight change in her physical condition, a sensor collects the data and sends it to a server. The server then performs an assessment based on her heart rate and activity level, and based on the results, suggests that she consider purchasing relaxation items. This allows pregnant women to receive appropriate support regarding their health immediately at a physical store.

[0131] The following is an example of a prompt message to input into the generative AI model.

[0132] "Develop a promotional strategy to suggest appropriate products based on pregnant women's health data."

[0133] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0134] Step 1:

[0135] The device collects physiological data from the pregnant woman's wearable device. It takes heart rate and activity level as input and prepares to send it to the server via a transmission means. At this stage, the data is appropriately formatted and converted into a state that can be transmitted.

[0136] Step 2:

[0137] The server receives physiological data transmitted from the terminal. The input data is passed to an AI algorithm for analysis to assess the pregnant woman's health status. Data processing involves calculating heart rate change patterns and average values, and comparing these with activity levels to evaluate health status. The output is the health status assessment result.

[0138] Step 3:

[0139] The server uses a generation method to construct personalized nutrition and exercise advice based on the analysis results. This step uses health assessment results as input. Using AI, it references similar past data and the latest medical information to generate advice best suited to the pregnant woman's condition. This advice becomes the output.

[0140] Step 4:

[0141] The server sends the generated advice as a push notification to the user's smartphone via a notification system. The user receives the advice on their device and can use it to manage their own health.

[0142] Step 5:

[0143] The server transmits health data to a display device when a pregnant woman enters the store, and then displays appropriate product and service suggestions on a screen installed in the store. It uses the latest health data as input and combines it with store-specific promotional information to generate product suggestions. The suggestions displayed visually on the screen are the output.

[0144] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0145] This invention is a system that provides comprehensive health support for pregnant women, and by combining it with an emotional engine, it also provides support that includes the user's mental health care. The following describes each component and its operation.

[0146] Sensor means and transmission means:

[0147] The device collects physiological data from pregnant women, monitoring data such as heart rate, activity level, and sleep patterns in real time. The collected data is transmitted to a server via a secure communication protocol.

[0148] Server means and generation means:

[0149] The server analyzes the received data and assesses the health status of pregnant women. Based on this analysis, a generation system generates nutritional and exercise advice for each pregnant woman. In addition to health indicators, the analysis also takes into account the pregnant woman's lifestyle and past health.

[0150] Emotional engine:

[0151] The server inputs the user's voice and text data into an emotion engine to evaluate the user's emotional state. Based on this evaluation, the system determines the pregnant woman's mental state and provides appropriate support and advice.

[0152] Means of notification:

[0153] The system notifies the user's smart device of advice and analysis results from the emotion engine, which have been adjusted by the generation method. This notification includes information tailored to the pregnant woman's current emotional and health status, providing the user with empathetic support.

[0154] AI methods and update methods:

[0155] AI-powered systems provide rapid responses to user inquiries. Furthermore, the servers regularly acquire the latest medical information and update the database, ensuring that advice is provided based on highly reliable data.

[0156] Specific example:

[0157] For example, if a pregnant woman is experiencing stress,

[0158] User: Uses a smartphone app to consult about recent fatigue via voice.

[0159] Server: Analyzes voice data using an emotion engine to confirm that the user is in a high-stress state.

[0160] Generation method: Adjusting special breathing techniques and relaxation advice for stress management.

[0161] Notification method: Tailored advice is immediately notified to the user's device.

[0162] User: By following the advice provided and working on stress management, I feel mentally better the next day.

[0163] Thus, the system of the present invention comprehensively supports the physical and mental anxieties that pregnant women experience, enabling them to spend their pregnancy with peace of mind.

[0164] The following describes the processing flow.

[0165] Step 1:

[0166] The device records the pregnant woman's physiological data. For example, it measures heart rate, activity level, and sleep patterns using sensors and creates a data log.

[0167] Step 2:

[0168] The device sends the collected data to the server. The data is transferred to the server via Wi-Fi or Bluetooth using a secure communication protocol.

[0169] Step 3:

[0170] The server receives physiological data transmitted from the terminal. The received data is input into the analysis engine, preparing it to evaluate the health status.

[0171] Step 4:

[0172] The server uses an analysis engine to assess health status. AI algorithms identify normal physiological changes and abnormalities, detecting anomalies in the data.

[0173] Step 5:

[0174] The server runs an emotion engine that analyzes the emotional state of the user's voice and text. By evaluating stress levels and emotional tendencies, it understands the user's mood.

[0175] Step 6:

[0176] The generation mechanism creates personalized advice based on health status and emotional assessments. It designs advice that includes specific guidelines for nutrition, exercise, and stress management.

[0177] Step 7:

[0178] The server sends the generated advice to the user via a notification system. Push notifications are sent to smartphones and tablets so that users can check them immediately.

[0179] Step 8:

[0180] Users submit questions and feedback via the app. They communicate their questions and thoughts about the system through text or voice input.

[0181] Step 9:

[0182] The AI ​​system prepares answers based on user inquiries. The server generates the optimal answer from past data and FAQs and sends it to the user.

[0183] Step 10:

[0184] The server periodically retrieves medical information using update mechanisms and updates the database. This ensures that the advice generated based on the latest medical information is always up-to-date.

[0185] (Example 2)

[0186] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0187] Pregnant women require comprehensive health support because their physical and mental states fluctuate significantly during pregnancy. However, conventional health management systems have struggled to provide comprehensive support that takes into account the emotional state and lifestyle of individual users. Therefore, providing appropriate support tailored to the individual needs of pregnant women remains a challenge.

[0188] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0189] In this invention, the server includes information gathering means, data transfer means, information processing means, instruction generation means, information notification means, and emotion evaluation means. This makes it possible to provide optimal nutrition and exercise advice to individual pregnant women based on physiological data, and furthermore, to provide comprehensive mental support that also takes into account their emotional state.

[0190] "Information gathering means" refers to a device or system for continuously collecting physiological data and activity-related information.

[0191] "Data transfer means" refers to a device or protocol that has the function of transmitting collected data to another system via a communication line.

[0192] "Information processing means" refers to a system or software for analyzing received data and evaluating an individual's health condition.

[0193] A "directive generation means" is a system or algorithm that derives the optimal nutrition and exercise plan for the user based on the analysis results.

[0194] An "information notification means" is a device or function that conveys generated instructions or advice to the user.

[0195] An "emotional assessment tool" is a system or tool for analyzing a user's emotional state and taking appropriate action based on that analysis.

[0196] A "knowledge description means" is a system or algorithm for generating appropriate answers to user inquiries.

[0197] A "data update mechanism" is a system that has the function of acquiring the latest health-related information and updating the database within the system.

[0198] The system in this invention is designed to comprehensively support the health status of pregnant women. The system achieves personalized health management through diverse data processing between terminals, servers, and users.

[0199] The system uses wearable devices and smartphones to continuously record the pregnant woman's heart rate, activity level, and sleep patterns. These devices use Bluetooth technology to collect data in real time. The collected data is transmitted to a server via Wi-Fi or a mobile network using a secure communication protocol (e.g., SSL / TLS).

[0200] The server analyzes the received data using analysis software (including Python libraries such as Pandas and NumPy). Based on the analysis results, a generative AI model (for example, the Transformers natural language processing library) generates nutrition and exercise advice. The server also analyzes voice and text data using an emotion assessment system and evaluates the user's emotional state using Google Cloud's Natural Language API and other tools.

[0201] The generated advice and sentiment assessment results are notified to the user's smart device via Firebase Cloud Messaging. Users receive the notifications and incorporate them into their daily lives to manage their health. For example, they might incorporate specific exercises or practice relaxation techniques according to stress management advice.

[0202] As a concrete example, if a user is experiencing stress, the following prompt message can be used.

[0203] "I've been feeling really tired lately, and I'd like some advice on how to reduce stress."

[0204] The server analyzes this prompt and uses a generative AI model to provide a highly relevant response. Users can then use the provided advice to work towards improving their mental health.

[0205] In this way, this system provides effective support to pregnant women, enabling them to have a safe and secure pregnancy through comprehensive health support.

[0206] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0207] Step 1:

[0208] The device collects physiological data such as heart rate, activity level, and sleep patterns from the pregnant woman's wearable device or smartphone. These devices use Bluetooth to collect data in real time. The input is physiological data from the sensors, and the output is a data packet containing this data. The device checks the integrity of the data and retries if there are missing values.

[0209] Step 2:

[0210] The device collects data and sends it to the server via Wi-Fi or a mobile network. The data is encrypted using SSL / TLS to ensure security. Input is data packets, and output is the data sent to the server. The device waits for an ACK response to confirm successful transmission and retransmits the data if necessary.

[0211] Step 3:

[0212] The server analyzes the received data. Using Python's Pandas and NumPy libraries, data preprocessing and analysis are performed to assess the health status of pregnant women. Input is physiological data sent from the terminal, and output is the analysis results. By sorting the data chronologically and performing statistical analysis, outliers and patterns are detected.

[0213] Step 4:

[0214] The server uses a generative AI model based on the analysis results to generate nutrition and exercise advice. Leveraging the Hugging Face Transformers library, the advice is generated in natural language. The input is analyzed health data, and the output is text-based advice. This generation process also references the latest information from medical databases.

[0215] Step 5:

[0216] The server inputs user voice and text data into an emotion assessment system and evaluates the emotional state. It uses Google Cloud's Natural Language API for voice analysis and calculates an emotion score. Input is user voice or text, and output is the emotion assessment result. The emotion model's accuracy is improved through machine learning.

[0217] Step 6:

[0218] The server sends the generated advice and sentiment evaluation results to the user's smart device using an information notification system. Notifications are sent in real time using Firebase Cloud Messaging. The input is the generated advice and sentiment evaluation results, and the output is a notification displayed on the user's device. The notification is formatted in a concise and easy-to-understand manner.

[0219] Step 7:

[0220] The user receives notifications and takes action to manage their health accordingly. This might involve performing specific exercises or eating meals based on nutritional advice. The input is the notifications received on the device, and the output is the improvement in the user's health status. The app records the progress of these efforts to help with future data collection.

[0221] Step 8:

[0222] The server processes user inquiries using knowledge description tools and quickly generates answers using an AI model. It utilizes OpenAI's (registered trademark) large-scale language model to extract relevant information and generate appropriate responses. Input is the user's question, and output is the generated answer. The answers are reviewed by medical professionals to ensure reliability.

[0223] (Application Example 2)

[0224] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0225] For pregnant women to receive products and services that are better suited to their health and emotional state, effective and timely information sharing and accurate advice from specialized store staff are necessary. However, conventional systems have struggled to provide real-time responses tailored to the individual circumstances of pregnant women, resulting in a limitation to providing generic services.

[0226] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0227] In this invention, the server includes detection means for continuously collecting the physiological state of a pregnant woman, transmission means for transmitting the data acquired from the detection means via a communication medium, and creation means for generating individual health and behavioral guidelines in accordance with the pregnant woman's health status and emotional evaluation. This enables real-time analysis of the pregnant woman's health status and the provision of optimal products and services.

[0228] "Detection means" refers to a device or system that has the function of continuously collecting information on the physiological state of a pregnant woman.

[0229] "Transmission means" refers to a device or process that has the function of transmitting data acquired from detection means to a server via a communication medium.

[0230] A "processing means" is a system that has the capability to receive and analyze data sent by a transmission means.

[0231] "Generative means" refers to a device or process that has the function of generating individual health and behavioral guidelines based on the health status and emotional assessment of pregnant women.

[0232] "Notification means" refers to a device or process for informing the user of guidelines generated by the creation means.

[0233] "Distribution method" refers to a system that provides pregnant women with the most suitable products or services based on notification methods.

[0234] "Artificial intelligence means" refers to a system capable of generating optimal solutions based on the consultation content received from pregnant women.

[0235] A "new information acquisition means" is a device or system that has the function of acquiring the latest medical and health-related information and updating the data used in the creation means.

[0236] This invention is a system aimed at monitoring the health and emotional state of pregnant women in real time and providing them with the most suitable products and services. The specific implementation method is described below.

[0237] First, the user's device incorporates a detection mechanism that continuously collects physiological data such as the pregnant woman's heart rate and activity level. Smartwatches and fitness trackers are commonly used as sensor devices. The collected data is transmitted to a server via a secure communication protocol (e.g., HTTPS) using a transmission mechanism.

[0238] The server analyzes the received physiological data through processing devices to assess the pregnant woman's health. During this process, sentiment analysis can be performed using software such as the Google Cloud Natural Language API. Based on the analysis results, personalized health advice is generated by a creation device. This advice is instantly sent to the user's smartphone using a notification device.

[0239] Furthermore, store staff can use artificial intelligence to quickly respond to specific inquiries from users. For example, if a user inquires about the cause of their fatigue or stress, the AI ​​will compare it with past data and provide appropriate advice. Based on the generated health advice, a distribution system is used to deliver the most suitable products and services to the user.

[0240] This system also incorporates a means of acquiring new information, regularly obtaining the latest medical information and updating the database. This updated information is reflected in the datasets used in the creation process, forming the basis for providing the most up-to-date and reliable service.

[0241] As a concrete example, a user can notify the store of their health assessment via the app before visiting, allowing store staff to immediately suggest products and services based on that assessment upon arrival. An example of a prompt message could be, "Based on the pregnant woman's health data and sentiment analysis, please suggest suitable health products and mental care services."

[0242] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0243] Step 1:

[0244] The user's device continuously collects physiological data such as heart rate and activity level using wearable devices such as smartwatches. The input is the physiological data from the wearable device, and the output is this data being stored in the device's application.

[0245] Step 2:

[0246] The terminal transmits the collected physiological data to the server using a communication protocol (e.g., HTTPS). The input is the stored physiological data, and the output is the transmission and reception of this data to and from the server.

[0247] Step 3:

[0248] The server analyzes the received data using processing tools and performs a health assessment. The input is physiological data sent to the server, and the output is the health status assessment result based on this data. The server can also perform sentiment analysis using the Google Cloud Natural Language API.

[0249] Step 4:

[0250] The server generates personalized health advice based on the analyzed health assessment results. The input is the health assessment results, and the output is specific health advice for the user.

[0251] Step 5:

[0252] The server sends the generated advice to the user's smartphone via a notification system. The input is individual health advice, and the output is the notification of this advice to the user's device.

[0253] Step 6:

[0254] The user takes action based on the advice they receive, for example, deciding to visit a store. The user notifies the store of their status via their device. The input is the health advice received, and the output is the user's action based on it.

[0255] Step 7:

[0256] The server uses artificial intelligence to generate the optimal solution based on the user's inquiry. The input is the user's inquiry, and the output is the appropriate solution generated by the AI.

[0257] Step 8:

[0258] The server periodically acquires the latest medical information using new information acquisition methods and updates the database. The input is medical information acquired from external sources, and the output is an updated health information database.

[0259] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0260] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0261] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0262] [Second Embodiment]

[0263] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0264] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0265] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0266] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0267] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0268] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0269] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0270] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0271] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0272] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0273] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0274] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0275] The present invention is a system in which sensor means, transmission means, server means, generation means, notification means, AI means, and update means work in cooperation with each other to provide individualized health support for pregnant women. The operation of each component is described below.

[0276] Sensor means and transmission means:

[0277] A wearable device worn by the pregnant woman functions as a sensor, continuously collecting physiological data such as heart rate and sleep patterns. This data is transmitted to a server via a transmission device at regular intervals using a secure connection.

[0278] Server means and generation means:

[0279] The server means receives the data sent from the transmission means and performs analysis. The analysis is carried out by an AI algorithm and evaluates the health status of the pregnant woman. Based on the evaluated data, the generation means generates advice on nutrition and exercise according to the individual health status of the pregnant woman. The generated advice takes into account the progress of pregnancy and past data.

[0280] Notification means:

[0281] The generated advice is notified to the user in real time by the notification means. It is delivered as a push notification to the user's smartphone or tablet, and the user can receive the information at an appropriate timing.

[0282] AI means:

[0283] The user can input questions and anxieties in text or voice through the application. The AI means generates an optimal answer based on the input information, based on the FAQ database and past cases, and provides this as feedback to the user.

[0284] Update means:

[0285] The server means regularly obtains the latest medical information using an API for medical cooperation and updates the database. As a result, the advice provided by the generation means is always based on the latest information.

[0286] Specific example:

[0287] For example, suppose a pregnant woman feels a slight nausea around noon.

[0288] Terminal: The wearable device transmits the heart rate and activity level at this time to the server.

[0289] Server: Analyzes the data and confirms a slight increase in body temperature.

[0290] Generation method: Generate specific advice recommending hydration and rest to the user.

[0291] Notification method: Advice is delivered immediately to the user's device.

[0292] User: I followed the recommendations and my symptoms improved the next day.

[0293] This invention will allow pregnant women to manage their health with greater peace of mind and support them in having an ideal pregnancy.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The device collects physiological data from pregnant women. Specifically, it measures heart rate, activity level, sleep patterns, etc., using sensors in the wearable device and records the data at regular intervals.

[0297] Step 2:

[0298] The device transmits recorded physiological data. It then initiates a process to securely transmit the data to a server via wireless communication such as Wi-Fi or Bluetooth.

[0299] Step 3:

[0300] The server receives data sent from the terminal. It then passes the received data to the analysis engine and prepares it for analysis.

[0301] Step 4:

[0302] The server analyzes the received data using an AI model. During this process, it evaluates for any physiological changes that deviate from the normal range, thereby monitoring the user's health status.

[0303] Step 5:

[0304] The server creates nutrition and exercise advice by the generation means based on the analysis results, and devises individualized advice suitable for the health status and past data of the pregnant woman.

[0305] Step 6:

[0306] The server transmits the generated advice to the terminal using the notification means, and prepares to deliver the advice to the user's smart device as a push notification.

[0307] Step 7:

[0308] The terminal notifies the user of the advice. The advice is displayed on the user's smartphone or tablet by the push notification, and the user can check it immediately.

[0309] Step 8:

[0310] The user inputs questions and feedback using the application, and sends the questions to the system in text or voice via the interface of the application.

[0311] Step 9:

[0312] The server generates an answer using the AI means based on the user's question, and constructs an optimal answer by utilizing FAQ data and past cases.

[0313] Step 10:

[0314] The server sends the generated answer to the terminal and notifies the user. The user can check the answer on the application and obtain information to feel at ease or solve doubts.

[0315] (Example 1)

[0316] Next, we will describe Example 1. 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."

[0317] Pregnant women experience various physiological changes during pregnancy, and appropriate health management is required in response to these changes. However, it is difficult to continuously provide appropriate advice based on individual health information. Furthermore, there are limited means to quickly and accurately answer the anxieties and questions that pregnant women have. In addition, it is necessary to effectively incorporate the latest medical information. There is a need to provide a system that can solve these problems and support pregnant women in having a safe and secure pregnancy.

[0318] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0319] In this invention, the server includes a detection means for collecting information on the physiological processes of pregnant women, a transmission means for communicating the information from the detection means, and an information processing means for receiving the information communicated from the transmission means and performing analysis using an artificial intelligence algorithm. This enables the provision of appropriate nutrition and exercise advice based on the individual health condition of pregnant women, prompt answers to questions, and the incorporation of the latest medical information.

[0320] "Detection means" refers to devices or technologies that continuously sense and collect information about a pregnant woman's menstrual cycle.

[0321] "Transmission means" refers to a device or technology for transmitting information obtained by the detection means to other devices or servers using an appropriate communication protocol.

[0322] "Information processing means" refers to devices and algorithms used to analyze received information, and in particular, those that utilize artificial intelligence technology to evaluate information and generate analysis results.

[0323] "Generation means" refers to a device or technology that generates nutritional and exercise guidelines suitable for the health condition of pregnant women based on the results analyzed by information processing means, and utilizes a generation AI model.

[0324] "Notification means" refers to devices or services used to provide pregnant women and their related parties with guidelines created by generation means, and which have the function of notifying information terminals.

[0325] "Artificial intelligence means" refers to a system or device that uses AI technology, including natural language processing technology, to generate appropriate responses to user questions and requests.

[0326] "Information update methods" refer to processes and technologies for obtaining the latest medical information from external sources and updating data within a system based on that information.

[0327] This invention is a system that provides individualized support for the health management of pregnant women, and operates by coordinating sensor means, transmission means, server means, generation means, notification means, artificial intelligence means, and information update means.

[0328] Sensor means and transmission means:

[0329] The device (wearable device) has the function of acquiring physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns. This data is continuously collected by sensors installed on the device. The collected data is then securely encrypted and transmitted to the server via a communication protocol using a transmission method.

[0330] Server means and generation means:

[0331] The server receives the transmitted data, stores it in a database, and analyzes it using artificial intelligence algorithms. Machine learning models (e.g., deep learning networks) are used in the analysis. Based on the analysis results, the server generates individualized nutrition and exercise guidelines tailored to the pregnant woman's health condition. The generation method utilizes a generative AI model to generate information based on the latest medical guidelines.

[0332] Means of notification:

[0333] The generated health guidelines are transmitted in real time to the user's smart device via a notification system. Through this notification, the user can quickly receive appropriate health information.

[0334] Artificial intelligence tools:

[0335] Users can input questions and concerns through the application. The server analyzes the input information and uses natural language processing technology to generate and provide feedback the most appropriate answers from an FAQ database and past cases. For example, a prompt sentence for the generating AI model might be, "Please tell me about safe exercise during pregnancy."

[0336] Information update method:

[0337] The server periodically retrieves the latest medical information using a medical collaboration API and updates the system's data based on that information. This ensures that the information provided to users is always up-to-date.

[0338] A concrete example would be a scenario where, if a pregnant woman experiences mild nausea during the day, the device sends her heart rate and activity level to a server. The server then analyzes the information, generates appropriate guidance, and sends the user advice recommending rest and fluid intake. In this way, pregnant women can accurately understand their health status and confidently take appropriate action.

[0339] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0340] Step 1:

[0341] The device (wearable device) continuously collects physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns using sensors. This information is temporarily stored in digital format within the device. Physiological information is acquired as input, and an encrypted data package is generated as output.

[0342] Step 2:

[0343] The terminal transmits collected physiological information to the server via a secure communication protocol (e.g., HTTPS). Data integrity checks are performed during transmission. Encrypted data packages are used as input, and the output is the data after transmission to the server is complete.

[0344] Step 3:

[0345] The server receives data sent from the terminal and checks its security. Once the data's integrity and completeness are confirmed, it is stored in the database. The input is physiological information received from the terminal, and the output is stored in the database.

[0346] Step 4:

[0347] The server analyzes the stored data using machine learning algorithms (e.g., deep learning) to assess the health status of pregnant women. Physiological information obtained from a database is used as input, and the analysis results are output.

[0348] Step 5:

[0349] The server (generation mechanism) uses a generation AI model based on the analysis results to generate individual nutrition and exercise guidelines for pregnant women. The latest medical guidelines are also taken into consideration. The input is the analysis results, and the output is health guidelines.

[0350] Step 6:

[0351] The server (notification mechanism) sends the generated health guidelines as a push notification to the user's smart device. The input is the generated health guidelines, and the output is the notification received on the user's device.

[0352] Step 7:

[0353] Users receive information through the application and, if necessary, input further questions or concerns via text or voice. The input consists of the user's responses or questions, and the output is feedback to the user interface.

[0354] Step 8:

[0355] The server (AI) analyzes the user's question and generates the optimal answer using natural language processing technology. The prompt used is "Use the generative AI model to generate an answer to the pregnant woman's question, 'What kind of exercise is safe during pregnancy?'" The input is the user's question, and the output is the generated answer, which is then fed back to the user.

[0356] Step 9:

[0357] The server (update mechanism) uses a medical information API to retrieve the latest medical information and update the database. The input is medical information obtained from an external source, and the output is the updated database.

[0358] (Application Example 1)

[0359] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0360] Providing timely and optimal advice and suggestions tailored to each pregnant woman's health condition is challenging. Furthermore, creating an environment where pregnant women can receive immediate and helpful product and service recommendations when they visit a store is also difficult.

[0361] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0362] In this invention, the server includes a sensor means, a transmission means, and a generation means. This makes it possible to continuously collect physiological data of pregnant women, generate personalized nutrition and exercise advice based on that data, and suggest products or services suitable for customers.

[0363] "Sensing means" refers to devices or equipment that continuously collect physiological data from pregnant women.

[0364] "Transmission means" refers to a device or function that has the ability to transmit data acquired by a sensor to a server via a communication network.

[0365] "Server means" refers to a computing device or system that analyzes data received from the transmission means and evaluates the health status of pregnant women.

[0366] "Generation means" refers to the process or device that creates individualized nutrition and exercise advice tailored to the health condition of pregnant women based on the analyzed data.

[0367] "Notification means" refers to communication devices or functions for transmitting advice created by the generation means to the user in real time.

[0368] "Display means" refers to a visible output device or function that suggests suitable products or services based on the health data of the customer who visits the store.

[0369] The system that implements this application includes a sensor that works in conjunction with a wearable device worn by a pregnant woman to continuously collect physiological data. A server receives and analyzes the data transmitted from this sensor. This analysis includes a process of evaluating the pregnant woman's health status using an AI algorithm. Based on the evaluation results, the server generates nutrition and exercise advice tailored to the pregnant woman's individual health condition. This generated advice is delivered in real time to the pregnant woman's smartphone via a notification system.

[0370] Furthermore, the server is equipped with a display system that uses in-store displays to recommend appropriate products or services based on the real-time health data of pregnant women who visit the store. This allows pregnant women to receive product suggestions tailored to their health condition when they visit the store.

[0371] For example, if a pregnant woman visits a store and notices a slight change in her physical condition, a sensor collects the data and sends it to a server. The server then performs an assessment based on her heart rate and activity level, and based on the results, suggests that she consider purchasing relaxation items. This allows pregnant women to receive appropriate support regarding their health immediately at a physical store.

[0372] The following is an example of a prompt message to input into the generative AI model.

[0373] "Develop a promotional strategy to suggest appropriate products based on pregnant women's health data."

[0374] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0375] Step 1:

[0376] The device collects physiological data from the pregnant woman's wearable device. It takes heart rate and activity level as input and prepares to send it to the server via a transmission means. At this stage, the data is appropriately formatted and converted into a state that can be transmitted.

[0377] Step 2:

[0378] The server receives physiological data transmitted from the terminal. The input data is passed to an AI algorithm for analysis to assess the pregnant woman's health status. Data processing involves calculating heart rate change patterns and average values, and comparing these with activity levels to evaluate health status. The output is the health status assessment result.

[0379] Step 3:

[0380] The server uses a generation method to construct personalized nutrition and exercise advice based on the analysis results. This step uses health assessment results as input. Using AI, it references similar past data and the latest medical information to generate advice best suited to the pregnant woman's condition. This advice becomes the output.

[0381] Step 4:

[0382] The server sends the generated advice as a push notification to the user's smartphone via a notification system. The user receives the advice on their device and can use it to manage their own health.

[0383] Step 5:

[0384] The server transmits health data to a display device when a pregnant woman enters the store, and then displays appropriate product and service suggestions on a screen installed in the store. It uses the latest health data as input and combines it with store-specific promotional information to generate product suggestions. The suggestions displayed visually on the screen are the output.

[0385] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0386] This invention is a system that provides comprehensive health support for pregnant women, and by combining it with an emotional engine, it also provides support that includes the user's mental health care. The following describes each component and its operation.

[0387] Sensor means and transmission means:

[0388] The device collects physiological data from pregnant women, monitoring data such as heart rate, activity level, and sleep patterns in real time. The collected data is transmitted to a server via a secure communication protocol.

[0389] Server means and generation means:

[0390] The server analyzes the received data and assesses the health status of pregnant women. Based on this analysis, a generation system generates nutritional and exercise advice for each pregnant woman. In addition to health indicators, the analysis also takes into account the pregnant woman's lifestyle and past health.

[0391] Emotional engine:

[0392] The server inputs the user's voice and text data into an emotion engine to evaluate the user's emotional state. Based on this evaluation, the system determines the pregnant woman's mental state and provides appropriate support and advice.

[0393] Means of notification:

[0394] The system notifies the user's smart device of advice and analysis results from the emotion engine, which have been adjusted by the generation method. This notification includes information tailored to the pregnant woman's current emotional and health status, providing the user with empathetic support.

[0395] AI methods and update methods:

[0396] AI-powered systems provide rapid responses to user inquiries. Furthermore, the servers regularly acquire the latest medical information and update the database, ensuring that advice is provided based on highly reliable data.

[0397] Specific example:

[0398] For example, if a pregnant woman is experiencing stress,

[0399] User: Uses a smartphone app to consult about recent fatigue via voice.

[0400] Server: Analyzes voice data using an emotion engine to confirm that the user is in a high-stress state.

[0401] Generation method: Adjusting special breathing techniques and relaxation advice for stress management.

[0402] Notification method: Tailored advice is immediately notified to the user's device.

[0403] User: By following the advice provided and working on stress management, I feel mentally better the next day.

[0404] Thus, the system of the present invention comprehensively supports the physical and mental anxieties that pregnant women experience, enabling them to spend their pregnancy with peace of mind.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The device records the pregnant woman's physiological data. For example, it measures heart rate, activity level, and sleep patterns using sensors and creates a data log.

[0408] Step 2:

[0409] The device sends the collected data to the server. The data is transferred to the server via Wi-Fi or Bluetooth using a secure communication protocol.

[0410] Step 3:

[0411] The server receives physiological data transmitted from the terminal. The received data is input into the analysis engine, preparing it to evaluate the health status.

[0412] Step 4:

[0413] The server uses an analysis engine to assess health status. AI algorithms identify normal physiological changes and abnormalities, detecting anomalies in the data.

[0414] Step 5:

[0415] The server runs an emotion engine that analyzes the emotional state of the user's voice and text. By evaluating stress levels and emotional tendencies, it understands the user's mood.

[0416] Step 6:

[0417] The generation mechanism creates personalized advice based on health status and emotional assessments. It designs advice that includes specific guidelines for nutrition, exercise, and stress management.

[0418] Step 7:

[0419] The server sends the generated advice to the user via a notification system. Push notifications are sent to smartphones and tablets so that users can check them immediately.

[0420] Step 8:

[0421] Users submit questions and feedback via the app. They communicate their questions and thoughts about the system through text or voice input.

[0422] Step 9:

[0423] The AI ​​system prepares answers based on user inquiries. The server generates the optimal answer from past data and FAQs and sends it to the user.

[0424] Step 10:

[0425] The server periodically retrieves medical information using update mechanisms and updates the database. This ensures that the advice generated based on the latest medical information is always up-to-date.

[0426] (Example 2)

[0427] Next, we will describe Example 2. 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".

[0428] Pregnant women require comprehensive health support because their physical and mental states fluctuate significantly during pregnancy. However, conventional health management systems have struggled to provide comprehensive support that takes into account the emotional state and lifestyle of individual users. Therefore, providing appropriate support tailored to the individual needs of pregnant women remains a challenge.

[0429] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0430] In this invention, the server includes information gathering means, data transfer means, information processing means, instruction generation means, information notification means, and emotion evaluation means. This makes it possible to provide optimal nutrition and exercise advice to individual pregnant women based on physiological data, and furthermore, to provide comprehensive mental support that also takes into account their emotional state.

[0431] "Information gathering means" refers to a device or system for continuously collecting physiological data and activity-related information.

[0432] "Data transfer means" refers to a device or protocol that has the function of transmitting collected data to another system via a communication line.

[0433] "Information processing means" refers to a system or software for analyzing received data and evaluating an individual's health condition.

[0434] A "directive generation means" is a system or algorithm that derives the optimal nutrition and exercise plan for the user based on the analysis results.

[0435] An "information notification means" is a device or function that conveys generated instructions or advice to the user.

[0436] An "emotional assessment tool" is a system or tool for analyzing a user's emotional state and taking appropriate action based on that analysis.

[0437] A "knowledge description means" is a system or algorithm for generating appropriate answers to user inquiries.

[0438] A "data update mechanism" is a system that has the function of acquiring the latest health-related information and updating the database within the system.

[0439] The system in this invention is designed to comprehensively support the health status of pregnant women. The system achieves personalized health management through diverse data processing between terminals, servers, and users.

[0440] The system uses wearable devices and smartphones to continuously record the pregnant woman's heart rate, activity level, and sleep patterns. These devices use Bluetooth technology to collect data in real time. The collected data is transmitted to a server via Wi-Fi or a mobile network using a secure communication protocol (e.g., SSL / TLS).

[0441] The server analyzes the received data using analysis software (including Python libraries such as Pandas and NumPy). Based on the analysis results, a generative AI model (for example, the Transformers natural language processing library) generates nutrition and exercise advice. The server also analyzes voice and text data using an emotion assessment system and evaluates the user's emotional state using Google Cloud's Natural Language API and other tools.

[0442] The generated advice and sentiment assessment results are notified to the user's smart device via Firebase Cloud Messaging. Users receive the notifications and incorporate them into their daily lives to manage their health. For example, they might incorporate specific exercises or practice relaxation techniques according to stress management advice.

[0443] As a concrete example, if a user is experiencing stress, the following prompt message can be used.

[0444] "I've been feeling really tired lately, and I'd like some advice on how to reduce stress."

[0445] The server analyzes this prompt and uses a generative AI model to provide a highly relevant response. Users can then use the provided advice to work towards improving their mental health.

[0446] In this way, this system provides effective support to pregnant women, enabling them to have a safe and secure pregnancy through comprehensive health support.

[0447] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0448] Step 1:

[0449] The device collects physiological data such as heart rate, activity level, and sleep patterns from the pregnant woman's wearable device or smartphone. These devices use Bluetooth to collect data in real time. The input is physiological data from the sensors, and the output is a data packet containing this data. The device checks the integrity of the data and retries if there are missing values.

[0450] Step 2:

[0451] The device collects data and sends it to the server via Wi-Fi or a mobile network. The data is encrypted using SSL / TLS to ensure security. Input is data packets, and output is the data sent to the server. The device waits for an ACK response to confirm successful transmission and retransmits the data if necessary.

[0452] Step 3:

[0453] The server analyzes the received data. Using Python's Pandas and NumPy libraries, data preprocessing and analysis are performed to assess the health status of pregnant women. Input is physiological data sent from the terminal, and output is the analysis results. By sorting the data chronologically and performing statistical analysis, outliers and patterns are detected.

[0454] Step 4:

[0455] The server uses a generative AI model based on the analysis results to generate nutrition and exercise advice. Leveraging the Hugging Face Transformers library, the advice is generated in natural language. The input is analyzed health data, and the output is text-based advice. This generation process also references the latest information from medical databases.

[0456] Step 5:

[0457] The server inputs user voice and text data into an emotion assessment system and evaluates the emotional state. It uses Google Cloud's Natural Language API for voice analysis and calculates an emotion score. Input is user voice or text, and output is the emotion assessment result. The emotion model's accuracy is improved through machine learning.

[0458] Step 6:

[0459] The server sends the generated advice and sentiment evaluation results to the user's smart device using an information notification system. Notifications are sent in real time using Firebase Cloud Messaging. The input is the generated advice and sentiment evaluation results, and the output is a notification displayed on the user's device. The notification is formatted in a concise and easy-to-understand manner.

[0460] Step 7:

[0461] The user receives notifications and takes action to manage their health accordingly. This might involve performing specific exercises or eating meals based on nutritional advice. The input is the notifications received on the device, and the output is the improvement in the user's health status. The app records the progress of these efforts to help with future data collection.

[0462] Step 8:

[0463] The server processes user inquiries using knowledge description tools and quickly generates answers using an AI model. It utilizes OpenAI's large-scale language model to extract relevant information and generate appropriate responses. Input is the user's question, and output is the generated answer. The answers are reviewed by medical professionals to ensure reliability.

[0464] (Application Example 2)

[0465] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0466] For pregnant women to receive products and services that are better suited to their health and emotional state, effective and timely information sharing and accurate advice from specialized store staff are necessary. However, conventional systems have struggled to provide real-time responses tailored to the individual circumstances of pregnant women, resulting in a limitation to providing generic services.

[0467] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0468] In this invention, the server includes detection means for continuously collecting the physiological state of a pregnant woman, transmission means for transmitting the data acquired from the detection means via a communication medium, and creation means for generating individual health and behavioral guidelines in accordance with the pregnant woman's health status and emotional evaluation. This enables real-time analysis of the pregnant woman's health status and the provision of optimal products and services.

[0469] "Detection means" refers to a device or system that has the function of continuously collecting information on the physiological state of a pregnant woman.

[0470] "Transmission means" refers to a device or process that has the function of transmitting data acquired from detection means to a server via a communication medium.

[0471] A "processing means" is a system that has the capability to receive and analyze data sent by a transmission means.

[0472] "Generative means" refers to a device or process that has the function of generating individual health and behavioral guidelines based on the health status and emotional assessment of pregnant women.

[0473] "Notification means" refers to a device or process for informing the user of guidelines generated by the creation means.

[0474] "Distribution method" refers to a system that provides pregnant women with the most suitable products or services based on notification methods.

[0475] "Artificial intelligence means" refers to a system capable of generating optimal solutions based on the consultation content received from pregnant women.

[0476] A "new information acquisition means" is a device or system that has the function of acquiring the latest medical and health-related information and updating the data used in the creation means.

[0477] This invention is a system aimed at monitoring the health and emotional state of pregnant women in real time and providing them with the most suitable products and services. The specific implementation method is described below.

[0478] First, the user's device incorporates a detection mechanism that continuously collects physiological data such as the pregnant woman's heart rate and activity level. Smartwatches and fitness trackers are commonly used as sensor devices. The collected data is transmitted to a server via a secure communication protocol (e.g., HTTPS) using a transmission mechanism.

[0479] The server analyzes the received physiological data through processing devices to assess the pregnant woman's health. During this process, sentiment analysis can be performed using software such as the Google Cloud Natural Language API. Based on the analysis results, personalized health advice is generated by a creation device. This advice is instantly sent to the user's smartphone using a notification device.

[0480] Furthermore, store staff can use artificial intelligence to quickly respond to specific inquiries from users. For example, if a user inquires about the cause of their fatigue or stress, the AI ​​will compare it with past data and provide appropriate advice. Based on the generated health advice, a distribution system is used to deliver the most suitable products and services to the user.

[0481] This system also incorporates a means of acquiring new information, regularly obtaining the latest medical information and updating the database. This updated information is reflected in the datasets used in the creation process, forming the basis for providing the most up-to-date and reliable service.

[0482] As a concrete example, a user can notify the store of their health assessment via the app before visiting, allowing store staff to immediately suggest products and services based on that assessment upon arrival. An example of a prompt message could be, "Based on the pregnant woman's health data and sentiment analysis, please suggest suitable health products and mental care services."

[0483] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0484] Step 1:

[0485] The user's device continuously collects physiological data such as heart rate and activity level using wearable devices such as smartwatches. The input is the physiological data from the wearable device, and the output is this data being stored in the device's application.

[0486] Step 2:

[0487] The terminal transmits the collected physiological data to the server using a communication protocol (e.g., HTTPS). The input is the stored physiological data, and the output is the transmission and reception of this data to and from the server.

[0488] Step 3:

[0489] The server analyzes the received data using processing tools and performs a health assessment. The input is physiological data sent to the server, and the output is the health status assessment result based on this data. The server can also perform sentiment analysis using the Google Cloud Natural Language API.

[0490] Step 4:

[0491] The server generates personalized health advice based on the analyzed health assessment results. The input is the health assessment results, and the output is specific health advice for the user.

[0492] Step 5:

[0493] The server sends the generated advice to the user's smartphone via a notification system. The input is individual health advice, and the output is the notification of this advice to the user's device.

[0494] Step 6:

[0495] The user takes action based on the advice they receive, for example, deciding to visit a store. The user notifies the store of their status via their device. The input is the health advice received, and the output is the user's action based on it.

[0496] Step 7:

[0497] The server uses artificial intelligence to generate the optimal solution based on the user's inquiry. The input is the user's inquiry, and the output is the appropriate solution generated by the AI.

[0498] Step 8:

[0499] The server periodically acquires the latest medical information using new information acquisition methods and updates the database. The input is medical information acquired from external sources, and the output is an updated health information database.

[0500] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0501] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0502] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0503] [Third Embodiment]

[0504] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0505] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0506] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0507] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0508] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0509] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0510] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0511] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0512] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0513] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0514] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0515] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0516] The present invention is a system in which sensor means, transmission means, server means, generation means, notification means, AI means, and update means work in cooperation with each other to provide individualized health support for pregnant women. The operation of each component is described below.

[0517] Sensor means and transmission means:

[0518] A wearable device worn by the pregnant woman functions as a sensor, continuously collecting physiological data such as heart rate and sleep patterns. This data is transmitted to a server via a transmission device at regular intervals using a secure connection.

[0519] Server means and generation means:

[0520] The server receives and analyzes data sent from the transmission unit. The analysis is performed by an AI algorithm to assess the health status of pregnant women. Based on the evaluated data, the generation unit generates nutritional and exercise advice tailored to the individual health status of each pregnant woman. The generated advice takes into account the progress of the pregnancy and past data.

[0521] Means of notification:

[0522] The generated advice is notified to the user in real time via a notification system. It is delivered as a push notification to the user's smartphone or tablet, allowing the user to receive the information at the appropriate time.

[0523] AI means:

[0524] Users can input questions and concerns via text or voice through the application. The AI ​​system uses the input information to generate the best possible answer based on an FAQ database and past cases, and then provides this answer as feedback to the user.

[0525] Update method:

[0526] The server system uses an API for medical collaboration to periodically retrieve the latest medical information and update the database. This ensures that the advice provided by the generation system is always based on the most up-to-date information.

[0527] Specific example:

[0528] For example, suppose a pregnant woman feels mild nausea around noon.

[0529] Terminal: The wearable device sends its current heart rate and activity level to the server.

[0530] Server: Analyzes the data and confirms a slight increase in body temperature.

[0531] Generation method: Generate specific advice recommending hydration and rest to the user.

[0532] Notification method: Advice is delivered immediately to the user's device.

[0533] User: I followed the recommendations and my symptoms improved the next day.

[0534] This invention will allow pregnant women to manage their health with greater peace of mind and support them in having an ideal pregnancy.

[0535] The following describes the processing flow.

[0536] Step 1:

[0537] The device collects physiological data from pregnant women. Specifically, it measures heart rate, activity level, sleep patterns, etc., using sensors in the wearable device and records the data at regular intervals.

[0538] Step 2:

[0539] The device transmits recorded physiological data. It then initiates a process to securely transmit the data to a server via wireless communication such as Wi-Fi or Bluetooth.

[0540] Step 3:

[0541] The server receives data sent from the terminal. It then passes the received data to the analysis engine and prepares it for analysis.

[0542] Step 4:

[0543] The server analyzes the received data using an AI model. During this process, it evaluates for any physiological changes that deviate from the normal range, thereby monitoring the user's health status.

[0544] Step 5:

[0545] The server generates nutrition and exercise advice based on the analysis results. It devises personalized advice tailored to the pregnant woman's health condition and past data.

[0546] Step 6:

[0547] The server sends the generated advice to the terminal using a notification method. It prepares to deliver the advice as a push notification to the user's smart device.

[0548] Step 7:

[0549] The device notifies the user of advice. Push notifications display the advice on the user's smartphone or tablet, allowing the user to check it immediately.

[0550] Step 8:

[0551] Users enter questions and feedback using the application. They send their questions to the system via text or voice through the application's interface.

[0552] Step 9:

[0553] The server generates answers using AI based on user questions. It utilizes FAQ data and past case studies to construct the optimal response.

[0554] Step 10:

[0555] The server sends the generated response to the terminal and notifies the user. The user can then check the response within the application, gain reassurance, and obtain information to resolve their questions.

[0556] (Example 1)

[0557] Next, we will describe Example 1. 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."

[0558] Pregnant women experience various physiological changes during pregnancy, and appropriate health management is required in response to these changes. However, it is difficult to continuously provide appropriate advice based on individual health information. Furthermore, there are limited means to quickly and accurately answer the anxieties and questions that pregnant women have. In addition, it is necessary to effectively incorporate the latest medical information. There is a need to provide a system that can solve these problems and support pregnant women in having a safe and secure pregnancy.

[0559] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0560] In this invention, the server includes a detection means for collecting information on the physiological processes of pregnant women, a transmission means for communicating the information from the detection means, and an information processing means for receiving the information communicated from the transmission means and performing analysis using an artificial intelligence algorithm. This enables the provision of appropriate nutrition and exercise advice based on the individual health condition of pregnant women, prompt answers to questions, and the incorporation of the latest medical information.

[0561] "Detection means" refers to devices or technologies that continuously sense and collect information about a pregnant woman's menstrual cycle.

[0562] "Transmission means" refers to a device or technology for transmitting information obtained by the detection means to other devices or servers using an appropriate communication protocol.

[0563] "Information processing means" refers to devices and algorithms used to analyze received information, and in particular, those that utilize artificial intelligence technology to evaluate information and generate analysis results.

[0564] "Generation means" refers to a device or technology that generates nutritional and exercise guidelines suitable for the health condition of pregnant women based on the results analyzed by information processing means, and utilizes a generation AI model.

[0565] "Notification means" refers to devices or services used to provide pregnant women and their related parties with guidelines created by generation means, and which have the function of notifying information terminals.

[0566] "Artificial intelligence means" refers to a system or device that uses AI technology, including natural language processing technology, to generate appropriate responses to user questions and requests.

[0567] "Information update methods" refer to processes and technologies for obtaining the latest medical information from external sources and updating data within a system based on that information.

[0568] This invention is a system that provides individualized support for the health management of pregnant women, and operates by coordinating sensor means, transmission means, server means, generation means, notification means, artificial intelligence means, and information update means.

[0569] Sensor means and transmission means:

[0570] The device (wearable device) has the function of acquiring physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns. This data is continuously collected by sensors installed on the device. The collected data is then securely encrypted and transmitted to the server via a communication protocol using a transmission method.

[0571] Server means and generation means:

[0572] The server receives the transmitted data, stores it in a database, and analyzes it using artificial intelligence algorithms. Machine learning models (e.g., deep learning networks) are used in the analysis. Based on the analysis results, the server generates individualized nutrition and exercise guidelines tailored to the pregnant woman's health condition. The generation method utilizes a generative AI model to generate information based on the latest medical guidelines.

[0573] Means of notification:

[0574] The generated health guidelines are transmitted in real time to the user's smart device via a notification system. Through this notification, the user can quickly receive appropriate health information.

[0575] Artificial intelligence tools:

[0576] Users can input questions and concerns through the application. The server analyzes the input information and uses natural language processing technology to generate and provide feedback the most appropriate answers from an FAQ database and past cases. For example, a prompt sentence for the generating AI model might be, "Please tell me about safe exercise during pregnancy."

[0577] Information update method:

[0578] The server periodically retrieves the latest medical information using a medical collaboration API and updates the system's data based on that information. This ensures that the information provided to users is always up-to-date.

[0579] A concrete example would be a scenario where, if a pregnant woman experiences mild nausea during the day, the device sends her heart rate and activity level to a server. The server then analyzes the information, generates appropriate guidance, and sends the user advice recommending rest and fluid intake. In this way, pregnant women can accurately understand their health status and confidently take appropriate action.

[0580] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0581] Step 1:

[0582] The device (wearable device) continuously collects physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns using sensors. This information is temporarily stored in digital format within the device. Physiological information is acquired as input, and an encrypted data package is generated as output.

[0583] Step 2:

[0584] The terminal transmits collected physiological information to the server via a secure communication protocol (e.g., HTTPS). Data integrity checks are performed during transmission. Encrypted data packages are used as input, and the output is the data after transmission to the server is complete.

[0585] Step 3:

[0586] The server receives data sent from the terminal and checks its security. Once the data's integrity and completeness are confirmed, it is stored in the database. The input is physiological information received from the terminal, and the output is stored in the database.

[0587] Step 4:

[0588] The server analyzes the stored data using machine learning algorithms (e.g., deep learning) to assess the health status of pregnant women. Physiological information obtained from a database is used as input, and the analysis results are output.

[0589] Step 5:

[0590] The server (generation mechanism) uses a generation AI model based on the analysis results to generate individual nutrition and exercise guidelines for pregnant women. The latest medical guidelines are also taken into consideration. The input is the analysis results, and the output is health guidelines.

[0591] Step 6:

[0592] The server (notification mechanism) sends the generated health guidelines as a push notification to the user's smart device. The input is the generated health guidelines, and the output is the notification received on the user's device.

[0593] Step 7:

[0594] Users receive information through the application and, if necessary, input further questions or concerns via text or voice. The input consists of the user's responses or questions, and the output is feedback to the user interface.

[0595] Step 8:

[0596] The server (AI) analyzes the user's question and generates the optimal answer using natural language processing technology. The prompt used is "Use the generative AI model to generate an answer to the pregnant woman's question, 'What kind of exercise is safe during pregnancy?'" The input is the user's question, and the output is the generated answer, which is then fed back to the user.

[0597] Step 9:

[0598] The server (update mechanism) uses a medical information API to retrieve the latest medical information and update the database. The input is medical information obtained from an external source, and the output is the updated database.

[0599] (Application Example 1)

[0600] Next, we will explain Application Example 1. In the following explanation, 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."

[0601] Providing timely and optimal advice and suggestions tailored to each pregnant woman's health condition is challenging. Furthermore, creating an environment where pregnant women can receive immediate and helpful product and service recommendations when they visit a store is also difficult.

[0602] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0603] In this invention, the server includes a sensor means, a transmission means, and a generation means. This makes it possible to continuously collect physiological data of pregnant women, generate personalized nutrition and exercise advice based on that data, and suggest products or services suitable for customers.

[0604] "Sensing means" refers to devices or equipment that continuously collect physiological data from pregnant women.

[0605] "Transmission means" refers to a device or function that has the ability to transmit data acquired by a sensor to a server via a communication network.

[0606] "Server means" refers to a computing device or system that analyzes data received from the transmission means and evaluates the health status of pregnant women.

[0607] "Generation means" refers to the process or device that creates individualized nutrition and exercise advice tailored to the health condition of pregnant women based on the analyzed data.

[0608] "Notification means" refers to communication devices or functions for transmitting advice created by the generation means to the user in real time.

[0609] "Display means" refers to a visible output device or function that suggests suitable products or services based on the health data of the customer who visits the store.

[0610] The system that implements this application includes a sensor that works in conjunction with a wearable device worn by a pregnant woman to continuously collect physiological data. A server receives and analyzes the data transmitted from this sensor. This analysis includes a process of evaluating the pregnant woman's health status using an AI algorithm. Based on the evaluation results, the server generates nutrition and exercise advice tailored to the pregnant woman's individual health condition. This generated advice is delivered in real time to the pregnant woman's smartphone via a notification system.

[0611] Furthermore, the server is equipped with a display system that uses in-store displays to recommend appropriate products or services based on the real-time health data of pregnant women who visit the store. This allows pregnant women to receive product suggestions tailored to their health condition when they visit the store.

[0612] For example, if a pregnant woman visits a store and notices a slight change in her physical condition, a sensor collects the data and sends it to a server. The server then performs an assessment based on her heart rate and activity level, and based on the results, suggests that she consider purchasing relaxation items. This allows pregnant women to receive appropriate support regarding their health immediately at a physical store.

[0613] The following is an example of a prompt message to input into the generative AI model.

[0614] "Develop a promotional strategy to suggest appropriate products based on pregnant women's health data."

[0615] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0616] Step 1:

[0617] The device collects physiological data from the pregnant woman's wearable device. It takes heart rate and activity level as input and prepares to send it to the server via a transmission means. At this stage, the data is appropriately formatted and converted into a state that can be transmitted.

[0618] Step 2:

[0619] The server receives physiological data transmitted from the terminal. The input data is passed to an AI algorithm for analysis to assess the pregnant woman's health status. Data processing involves calculating heart rate change patterns and average values, and comparing these with activity levels to evaluate health status. The output is the health status assessment result.

[0620] Step 3:

[0621] The server uses a generation method to construct personalized nutrition and exercise advice based on the analysis results. This step uses health assessment results as input. Using AI, it references similar past data and the latest medical information to generate advice best suited to the pregnant woman's condition. This advice becomes the output.

[0622] Step 4:

[0623] The server sends the generated advice as a push notification to the user's smartphone via a notification system. The user receives the advice on their device and can use it to manage their own health.

[0624] Step 5:

[0625] The server transmits health data to a display device when a pregnant woman enters the store, and then displays appropriate product and service suggestions on a screen installed in the store. It uses the latest health data as input and combines it with store-specific promotional information to generate product suggestions. The suggestions displayed visually on the screen are the output.

[0626] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0627] This invention is a system that provides comprehensive health support for pregnant women, and by combining it with an emotional engine, it also provides support that includes the user's mental health care. The following describes each component and its operation.

[0628] Sensor means and transmission means:

[0629] The device collects physiological data from pregnant women, monitoring data such as heart rate, activity level, and sleep patterns in real time. The collected data is transmitted to a server via a secure communication protocol.

[0630] Server means and generation means:

[0631] The server analyzes the received data and assesses the health status of pregnant women. Based on this analysis, a generation system generates nutritional and exercise advice for each pregnant woman. In addition to health indicators, the analysis also takes into account the pregnant woman's lifestyle and past health.

[0632] Emotional engine:

[0633] The server inputs the user's voice and text data into an emotion engine to evaluate the user's emotional state. Based on this evaluation, the system determines the pregnant woman's mental state and provides appropriate support and advice.

[0634] Means of notification:

[0635] The system notifies the user's smart device of advice and analysis results from the emotion engine, which have been adjusted by the generation method. This notification includes information tailored to the pregnant woman's current emotional and health status, providing the user with empathetic support.

[0636] AI methods and update methods:

[0637] AI-powered systems provide rapid responses to user inquiries. Furthermore, the servers regularly acquire the latest medical information and update the database, ensuring that advice is provided based on highly reliable data.

[0638] Specific example:

[0639] For example, if a pregnant woman is experiencing stress,

[0640] User: Uses a smartphone app to consult about recent fatigue via voice.

[0641] Server: Analyzes voice data using an emotion engine to confirm that the user is in a high-stress state.

[0642] Generation method: Adjusting special breathing techniques and relaxation advice for stress management.

[0643] Notification method: Tailored advice is immediately notified to the user's device.

[0644] User: By following the advice provided and working on stress management, I feel mentally better the next day.

[0645] Thus, the system of the present invention comprehensively supports the physical and mental anxieties that pregnant women experience, enabling them to spend their pregnancy with peace of mind.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The device records the pregnant woman's physiological data. For example, it measures heart rate, activity level, and sleep patterns using sensors and creates a data log.

[0649] Step 2:

[0650] The device sends the collected data to the server. The data is transferred to the server via Wi-Fi or Bluetooth using a secure communication protocol.

[0651] Step 3:

[0652] The server receives physiological data transmitted from the terminal. The received data is input into the analysis engine, preparing it to evaluate the health status.

[0653] Step 4:

[0654] The server uses an analysis engine to assess health status. AI algorithms identify normal physiological changes and abnormalities, detecting anomalies in the data.

[0655] Step 5:

[0656] The server runs an emotion engine that analyzes the emotional state of the user's voice and text. By evaluating stress levels and emotional tendencies, it understands the user's mood.

[0657] Step 6:

[0658] The generation mechanism creates personalized advice based on health status and emotional assessments. It designs advice that includes specific guidelines for nutrition, exercise, and stress management.

[0659] Step 7:

[0660] The server sends the generated advice to the user via a notification system. Push notifications are sent to smartphones and tablets so that users can check them immediately.

[0661] Step 8:

[0662] Users submit questions and feedback via the app. They communicate their questions and thoughts about the system through text or voice input.

[0663] Step 9:

[0664] The AI ​​system prepares answers based on user inquiries. The server generates the optimal answer from past data and FAQs and sends it to the user.

[0665] Step 10:

[0666] The server periodically retrieves medical information using update mechanisms and updates the database. This ensures that the advice generated based on the latest medical information is always up-to-date.

[0667] (Example 2)

[0668] Next, we will describe Example 2. 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."

[0669] Pregnant women require comprehensive health support because their physical and mental states fluctuate significantly during pregnancy. However, conventional health management systems have struggled to provide comprehensive support that takes into account the emotional state and lifestyle of individual users. Therefore, providing appropriate support tailored to the individual needs of pregnant women remains a challenge.

[0670] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0671] In this invention, the server includes information gathering means, data transfer means, information processing means, instruction generation means, information notification means, and emotion evaluation means. This makes it possible to provide optimal nutrition and exercise advice to individual pregnant women based on physiological data, and furthermore, to provide comprehensive mental support that also takes into account their emotional state.

[0672] "Information gathering means" refers to a device or system for continuously collecting physiological data and activity-related information.

[0673] "Data transfer means" refers to a device or protocol that has the function of transmitting collected data to another system via a communication line.

[0674] "Information processing means" refers to a system or software for analyzing received data and evaluating an individual's health condition.

[0675] A "directive generation means" is a system or algorithm that derives the optimal nutrition and exercise plan for the user based on the analysis results.

[0676] An "information notification means" is a device or function that conveys generated instructions or advice to the user.

[0677] An "emotional assessment tool" is a system or tool for analyzing a user's emotional state and taking appropriate action based on that analysis.

[0678] A "knowledge description means" is a system or algorithm for generating appropriate answers to user inquiries.

[0679] A "data update mechanism" is a system that has the function of acquiring the latest health-related information and updating the database within the system.

[0680] The system in this invention is designed to comprehensively support the health status of pregnant women. The system achieves personalized health management through diverse data processing between terminals, servers, and users.

[0681] The system uses wearable devices and smartphones to continuously record the pregnant woman's heart rate, activity level, and sleep patterns. These devices use Bluetooth technology to collect data in real time. The collected data is transmitted to a server via Wi-Fi or a mobile network using a secure communication protocol (e.g., SSL / TLS).

[0682] The server analyzes the received data using analysis software (including Python libraries such as Pandas and NumPy). Based on the analysis results, a generative AI model (for example, the Transformers natural language processing library) generates nutrition and exercise advice. The server also analyzes voice and text data using an emotion assessment system and evaluates the user's emotional state using Google Cloud's Natural Language API and other tools.

[0683] The generated advice and sentiment assessment results are notified to the user's smart device via Firebase Cloud Messaging. Users receive the notifications and incorporate them into their daily lives to manage their health. For example, they might incorporate specific exercises or practice relaxation techniques according to stress management advice.

[0684] As a concrete example, if a user is experiencing stress, the following prompt message can be used.

[0685] "I've been feeling really tired lately, and I'd like some advice on how to reduce stress."

[0686] The server analyzes this prompt and uses a generative AI model to provide a highly relevant response. Users can then use the provided advice to work towards improving their mental health.

[0687] In this way, this system provides effective support to pregnant women, enabling them to have a safe and secure pregnancy through comprehensive health support.

[0688] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0689] Step 1:

[0690] The device collects physiological data such as heart rate, activity level, and sleep patterns from the pregnant woman's wearable device or smartphone. These devices use Bluetooth to collect data in real time. The input is physiological data from the sensors, and the output is a data packet containing this data. The device checks the integrity of the data and retries if there are missing values.

[0691] Step 2:

[0692] The device collects data and sends it to the server via Wi-Fi or a mobile network. The data is encrypted using SSL / TLS to ensure security. Input is data packets, and output is the data sent to the server. The device waits for an ACK response to confirm successful transmission and retransmits the data if necessary.

[0693] Step 3:

[0694] The server analyzes the received data. Using Python's Pandas and NumPy libraries, data preprocessing and analysis are performed to assess the health status of pregnant women. Input is physiological data sent from the terminal, and output is the analysis results. By sorting the data chronologically and performing statistical analysis, outliers and patterns are detected.

[0695] Step 4:

[0696] The server uses a generative AI model based on the analysis results to generate nutrition and exercise advice. Leveraging the Hugging Face Transformers library, the advice is generated in natural language. The input is analyzed health data, and the output is text-based advice. This generation process also references the latest information from medical databases.

[0697] Step 5:

[0698] The server inputs user voice and text data into an emotion assessment system and evaluates the emotional state. It uses Google Cloud's Natural Language API for voice analysis and calculates an emotion score. Input is user voice or text, and output is the emotion assessment result. The emotion model's accuracy is improved through machine learning.

[0699] Step 6:

[0700] The server sends the generated advice and sentiment evaluation results to the user's smart device using an information notification system. Notifications are sent in real time using Firebase Cloud Messaging. The input is the generated advice and sentiment evaluation results, and the output is a notification displayed on the user's device. The notification is formatted in a concise and easy-to-understand manner.

[0701] Step 7:

[0702] The user receives notifications and takes action to manage their health accordingly. This might involve performing specific exercises or eating meals based on nutritional advice. The input is the notifications received on the device, and the output is the improvement in the user's health status. The app records the progress of these efforts to help with future data collection.

[0703] Step 8:

[0704] The server processes user inquiries using knowledge description tools and quickly generates answers using an AI model. It utilizes OpenAI's large-scale language model to extract relevant information and generate appropriate responses. Input is the user's question, and output is the generated answer. The answers are reviewed by medical professionals to ensure reliability.

[0705] (Application Example 2)

[0706] Next, we will explain application example 2. In the following explanation, 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."

[0707] For pregnant women to receive products and services that are better suited to their health and emotional state, effective and timely information sharing and accurate advice from specialized store staff are necessary. However, conventional systems have struggled to provide real-time responses tailored to the individual circumstances of pregnant women, resulting in a limitation to providing generic services.

[0708] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0709] In this invention, the server includes detection means for continuously collecting the physiological state of a pregnant woman, transmission means for transmitting the data acquired from the detection means via a communication medium, and creation means for generating individual health and behavioral guidelines in accordance with the pregnant woman's health status and emotional evaluation. This enables real-time analysis of the pregnant woman's health status and the provision of optimal products and services.

[0710] "Detection means" refers to a device or system that has the function of continuously collecting information on the physiological state of a pregnant woman.

[0711] "Transmission means" refers to a device or process that has the function of transmitting data acquired from detection means to a server via a communication medium.

[0712] A "processing means" is a system that has the capability to receive and analyze data sent by a transmission means.

[0713] "Generative means" refers to a device or process that has the function of generating individual health and behavioral guidelines based on the health status and emotional assessment of pregnant women.

[0714] "Notification means" refers to a device or process for informing the user of guidelines generated by the creation means.

[0715] "Distribution method" refers to a system that provides pregnant women with the most suitable products or services based on notification methods.

[0716] "Artificial intelligence means" refers to a system capable of generating optimal solutions based on the consultation content received from pregnant women.

[0717] A "new information acquisition means" is a device or system that has the function of acquiring the latest medical and health-related information and updating the data used in the creation means.

[0718] This invention is a system aimed at monitoring the health and emotional state of pregnant women in real time and providing them with the most suitable products and services. The specific implementation method is described below.

[0719] First, the user's device incorporates a detection mechanism that continuously collects physiological data such as the pregnant woman's heart rate and activity level. Smartwatches and fitness trackers are commonly used as sensor devices. The collected data is transmitted to a server via a secure communication protocol (e.g., HTTPS) using a transmission mechanism.

[0720] The server analyzes the received physiological data through processing devices to assess the pregnant woman's health. During this process, sentiment analysis can be performed using software such as the Google Cloud Natural Language API. Based on the analysis results, personalized health advice is generated by a creation device. This advice is instantly sent to the user's smartphone using a notification device.

[0721] Furthermore, store staff can use artificial intelligence to quickly respond to specific inquiries from users. For example, if a user inquires about the cause of their fatigue or stress, the AI ​​will compare it with past data and provide appropriate advice. Based on the generated health advice, a distribution system is used to deliver the most suitable products and services to the user.

[0722] This system also incorporates a means of acquiring new information, regularly obtaining the latest medical information and updating the database. This updated information is reflected in the datasets used in the creation process, forming the basis for providing the most up-to-date and reliable service.

[0723] As a concrete example, a user can notify the store of their health assessment via the app before visiting, allowing store staff to immediately suggest products and services based on that assessment upon arrival. An example of a prompt message could be, "Based on the pregnant woman's health data and sentiment analysis, please suggest suitable health products and mental care services."

[0724] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0725] Step 1:

[0726] The user's device continuously collects physiological data such as heart rate and activity level using wearable devices such as smartwatches. The input is the physiological data from the wearable device, and the output is this data being stored in the device's application.

[0727] Step 2:

[0728] The terminal transmits the collected physiological data to the server using a communication protocol (e.g., HTTPS). The input is the stored physiological data, and the output is the transmission and reception of this data to and from the server.

[0729] Step 3:

[0730] The server analyzes the received data using processing tools and performs a health assessment. The input is physiological data sent to the server, and the output is the health status assessment result based on this data. The server can also perform sentiment analysis using the Google Cloud Natural Language API.

[0731] Step 4:

[0732] The server generates personalized health advice based on the analyzed health assessment results. The input is the health assessment results, and the output is specific health advice for the user.

[0733] Step 5:

[0734] The server sends the generated advice to the user's smartphone via a notification system. The input is individual health advice, and the output is the notification of this advice to the user's device.

[0735] Step 6:

[0736] The user takes action based on the advice they receive, for example, deciding to visit a store. The user notifies the store of their status via their device. The input is the health advice received, and the output is the user's action based on it.

[0737] Step 7:

[0738] The server uses artificial intelligence to generate the optimal solution based on the user's inquiry. The input is the user's inquiry, and the output is the appropriate solution generated by the AI.

[0739] Step 8:

[0740] The server periodically acquires the latest medical information using new information acquisition methods and updates the database. The input is medical information acquired from external sources, and the output is an updated health information database.

[0741] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0742] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0743] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0744] [Fourth Embodiment]

[0745] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0746] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0747] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0748] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0749] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0750] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0751] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0752] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0753] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0754] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0755] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0756] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0757] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0758] The present invention is a system in which sensor means, transmission means, server means, generation means, notification means, AI means, and update means work in cooperation with each other to provide individualized health support for pregnant women. The operation of each component is described below.

[0759] Sensor means and transmission means:

[0760] A wearable device worn by the pregnant woman functions as a sensor, continuously collecting physiological data such as heart rate and sleep patterns. This data is transmitted to a server via a transmission device at regular intervals using a secure connection.

[0761] Server means and generation means:

[0762] The server receives and analyzes data sent from the transmission unit. The analysis is performed by an AI algorithm to assess the health status of pregnant women. Based on the evaluated data, the generation unit generates nutritional and exercise advice tailored to the individual health status of each pregnant woman. The generated advice takes into account the progress of the pregnancy and past data.

[0763] Means of notification:

[0764] The generated advice is notified to the user in real time via a notification system. It is delivered as a push notification to the user's smartphone or tablet, allowing the user to receive the information at the appropriate time.

[0765] AI means:

[0766] Users can input questions and concerns via text or voice through the application. The AI ​​system uses the input information to generate the best possible answer based on an FAQ database and past cases, and then provides this answer as feedback to the user.

[0767] Update method:

[0768] The server system uses an API for medical collaboration to periodically retrieve the latest medical information and update the database. This ensures that the advice provided by the generation system is always based on the most up-to-date information.

[0769] Specific example:

[0770] For example, suppose a pregnant woman feels mild nausea around noon.

[0771] Terminal: The wearable device sends its current heart rate and activity level to the server.

[0772] Server: Analyzes the data and confirms a slight increase in body temperature.

[0773] Generation method: Generate specific advice recommending hydration and rest to the user.

[0774] Notification method: Advice is delivered immediately to the user's device.

[0775] User: I followed the recommendations and my symptoms improved the next day.

[0776] This invention will allow pregnant women to manage their health with greater peace of mind and support them in having an ideal pregnancy.

[0777] The following describes the processing flow.

[0778] Step 1:

[0779] The device collects physiological data from pregnant women. Specifically, it measures heart rate, activity level, sleep patterns, etc., using sensors in the wearable device and records the data at regular intervals.

[0780] Step 2:

[0781] The device transmits recorded physiological data. It then initiates a process to securely transmit the data to a server via wireless communication such as Wi-Fi or Bluetooth.

[0782] Step 3:

[0783] The server receives data sent from the terminal. It then passes the received data to the analysis engine and prepares it for analysis.

[0784] Step 4:

[0785] The server analyzes the received data using an AI model. During this process, it evaluates for any physiological changes that deviate from the normal range, thereby monitoring the user's health status.

[0786] Step 5:

[0787] The server generates nutrition and exercise advice based on the analysis results. It devises personalized advice tailored to the pregnant woman's health condition and past data.

[0788] Step 6:

[0789] The server sends the generated advice to the terminal using a notification method. It prepares to deliver the advice as a push notification to the user's smart device.

[0790] Step 7:

[0791] The device notifies the user of advice. Push notifications display the advice on the user's smartphone or tablet, allowing the user to check it immediately.

[0792] Step 8:

[0793] Users enter questions and feedback using the application. They send their questions to the system via text or voice through the application's interface.

[0794] Step 9:

[0795] The server generates answers using AI based on user questions. It utilizes FAQ data and past case studies to construct the optimal response.

[0796] Step 10:

[0797] The server sends the generated response to the terminal and notifies the user. The user can then check the response within the application, gain reassurance, and obtain information to resolve their questions.

[0798] (Example 1)

[0799] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0800] Pregnant women experience various physiological changes during pregnancy, and appropriate health management is required in response to these changes. However, it is difficult to continuously provide appropriate advice based on individual health information. Furthermore, there are limited means to quickly and accurately answer the anxieties and questions that pregnant women have. In addition, it is necessary to effectively incorporate the latest medical information. There is a need to provide a system that can solve these problems and support pregnant women in having a safe and secure pregnancy.

[0801] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0802] In this invention, the server includes a detection means for collecting information on the physiological processes of pregnant women, a transmission means for communicating the information from the detection means, and an information processing means for receiving the information communicated from the transmission means and performing analysis using an artificial intelligence algorithm. This enables the provision of appropriate nutrition and exercise advice based on the individual health condition of pregnant women, prompt answers to questions, and the incorporation of the latest medical information.

[0803] "Detection means" refers to devices or technologies that continuously sense and collect information about a pregnant woman's menstrual cycle.

[0804] "Transmission means" refers to a device or technology for transmitting information obtained by the detection means to other devices or servers using an appropriate communication protocol.

[0805] "Information processing means" refers to devices and algorithms used to analyze received information, and in particular, those that utilize artificial intelligence technology to evaluate information and generate analysis results.

[0806] "Generation means" refers to a device or technology that generates nutritional and exercise guidelines suitable for the health condition of pregnant women based on the results analyzed by information processing means, and utilizes a generation AI model.

[0807] "Notification means" refers to devices or services used to provide pregnant women and their related parties with guidelines created by generation means, and which have the function of notifying information terminals.

[0808] "Artificial intelligence means" refers to a system or device that uses AI technology, including natural language processing technology, to generate appropriate responses to user questions and requests.

[0809] "Information update methods" refer to processes and technologies for obtaining the latest medical information from external sources and updating data within a system based on that information.

[0810] This invention is a system that provides individualized support for the health management of pregnant women, and operates by coordinating sensor means, transmission means, server means, generation means, notification means, artificial intelligence means, and information update means.

[0811] Sensor means and transmission means:

[0812] The device (wearable device) has the function of acquiring physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns. This data is continuously collected by sensors installed on the device. The collected data is then securely encrypted and transmitted to the server via a communication protocol using a transmission method.

[0813] Server means and generation means:

[0814] The server receives the transmitted data, stores it in a database, and analyzes it using artificial intelligence algorithms. Machine learning models (e.g., deep learning networks) are used in the analysis. Based on the analysis results, the server generates individualized nutrition and exercise guidelines tailored to the pregnant woman's health condition. The generation method utilizes a generative AI model to generate information based on the latest medical guidelines.

[0815] Means of notification:

[0816] The generated health guidelines are transmitted in real time to the user's smart device via a notification system. Through this notification, the user can quickly receive appropriate health information.

[0817] Artificial intelligence tools:

[0818] Users can input questions and concerns through the application. The server analyzes the input information and uses natural language processing technology to generate and provide feedback the most appropriate answers from an FAQ database and past cases. For example, a prompt sentence for the generating AI model might be, "Please tell me about safe exercise during pregnancy."

[0819] Information update method:

[0820] The server periodically retrieves the latest medical information using a medical collaboration API and updates the system's data based on that information. This ensures that the information provided to users is always up-to-date.

[0821] A concrete example would be a scenario where, if a pregnant woman experiences mild nausea during the day, the device sends her heart rate and activity level to a server. The server then analyzes the information, generates appropriate guidance, and sends the user advice recommending rest and fluid intake. In this way, pregnant women can accurately understand their health status and confidently take appropriate action.

[0822] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0823] Step 1:

[0824] The device (wearable device) continuously collects physiological information such as the pregnant woman's heart rate, body temperature, and sleep patterns using sensors. This information is temporarily stored in digital format within the device. Physiological information is acquired as input, and an encrypted data package is generated as output.

[0825] Step 2:

[0826] The terminal transmits collected physiological information to the server via a secure communication protocol (e.g., HTTPS). Data integrity checks are performed during transmission. Encrypted data packages are used as input, and the output is the data after transmission to the server is complete.

[0827] Step 3:

[0828] The server receives data sent from the terminal and checks its security. Once the data's integrity and completeness are confirmed, it is stored in the database. The input is physiological information received from the terminal, and the output is stored in the database.

[0829] Step 4:

[0830] The server analyzes the stored data using machine learning algorithms (e.g., deep learning) to assess the health status of pregnant women. Physiological information obtained from a database is used as input, and the analysis results are output.

[0831] Step 5:

[0832] The server (generation mechanism) uses a generation AI model based on the analysis results to generate individual nutrition and exercise guidelines for pregnant women. The latest medical guidelines are also taken into consideration. The input is the analysis results, and the output is health guidelines.

[0833] Step 6:

[0834] The server (notification mechanism) sends the generated health guidelines as a push notification to the user's smart device. The input is the generated health guidelines, and the output is the notification received on the user's device.

[0835] Step 7:

[0836] Users receive information through the application and, if necessary, input further questions or concerns via text or voice. The input consists of the user's responses or questions, and the output is feedback to the user interface.

[0837] Step 8:

[0838] The server (AI) analyzes the user's question and generates the optimal answer using natural language processing technology. The prompt used is "Use the generative AI model to generate an answer to the pregnant woman's question, 'What kind of exercise is safe during pregnancy?'" The input is the user's question, and the output is the generated answer, which is then fed back to the user.

[0839] Step 9:

[0840] The server (update mechanism) uses a medical information API to retrieve the latest medical information and update the database. The input is medical information obtained from an external source, and the output is the updated database.

[0841] (Application Example 1)

[0842] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0843] Providing timely and optimal advice and suggestions tailored to each pregnant woman's health condition is challenging. Furthermore, creating an environment where pregnant women can receive immediate and helpful product and service recommendations when they visit a store is also difficult.

[0844] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0845] In this invention, the server includes a sensor means, a transmission means, and a generation means. This makes it possible to continuously collect physiological data of pregnant women, generate personalized nutrition and exercise advice based on that data, and suggest products or services suitable for customers.

[0846] "Sensing means" refers to devices or equipment that continuously collect physiological data from pregnant women.

[0847] "Transmission means" refers to a device or function that has the ability to transmit data acquired by a sensor to a server via a communication network.

[0848] "Server means" refers to a computing device or system that analyzes data received from the transmission means and evaluates the health status of pregnant women.

[0849] "Generation means" refers to the process or device that creates individualized nutrition and exercise advice tailored to the health condition of pregnant women based on the analyzed data.

[0850] "Notification means" refers to communication devices or functions for transmitting advice created by the generation means to the user in real time.

[0851] "Display means" refers to a visible output device or function that suggests suitable products or services based on the health data of the customer who visits the store.

[0852] The system that implements this application includes a sensor that works in conjunction with a wearable device worn by a pregnant woman to continuously collect physiological data. A server receives and analyzes the data transmitted from this sensor. This analysis includes a process of evaluating the pregnant woman's health status using an AI algorithm. Based on the evaluation results, the server generates nutrition and exercise advice tailored to the pregnant woman's individual health condition. This generated advice is delivered in real time to the pregnant woman's smartphone via a notification system.

[0853] Furthermore, the server is equipped with a display system that uses in-store displays to recommend appropriate products or services based on the real-time health data of pregnant women who visit the store. This allows pregnant women to receive product suggestions tailored to their health condition when they visit the store.

[0854] For example, if a pregnant woman visits a store and notices a slight change in her physical condition, a sensor collects the data and sends it to a server. The server then performs an assessment based on her heart rate and activity level, and based on the results, suggests that she consider purchasing relaxation items. This allows pregnant women to receive appropriate support regarding their health immediately at a physical store.

[0855] The following is an example of a prompt message to input into the generative AI model.

[0856] "Develop a promotional strategy to suggest appropriate products based on pregnant women's health data."

[0857] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0858] Step 1:

[0859] The device collects physiological data from the pregnant woman's wearable device. It takes heart rate and activity level as input and prepares to send it to the server via a transmission means. At this stage, the data is appropriately formatted and converted into a state that can be transmitted.

[0860] Step 2:

[0861] The server receives physiological data transmitted from the terminal. The input data is passed to an AI algorithm for analysis to assess the pregnant woman's health status. Data processing involves calculating heart rate change patterns and average values, and comparing these with activity levels to evaluate health status. The output is the health status assessment result.

[0862] Step 3:

[0863] The server uses a generation method to construct personalized nutrition and exercise advice based on the analysis results. This step uses health assessment results as input. Using AI, it references similar past data and the latest medical information to generate advice best suited to the pregnant woman's condition. This advice becomes the output.

[0864] Step 4:

[0865] The server sends the generated advice as a push notification to the user's smartphone via a notification system. The user receives the advice on their device and can use it to manage their own health.

[0866] Step 5:

[0867] The server transmits health data to a display device when a pregnant woman enters the store, and then displays appropriate product and service suggestions on a screen installed in the store. It uses the latest health data as input and combines it with store-specific promotional information to generate product suggestions. The suggestions displayed visually on the screen are the output.

[0868] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0869] This invention is a system that provides comprehensive health support for pregnant women, and by combining it with an emotional engine, it also provides support that includes the user's mental health care. The following describes each component and its operation.

[0870] Sensor means and transmission means:

[0871] The device collects physiological data from pregnant women, monitoring data such as heart rate, activity level, and sleep patterns in real time. The collected data is transmitted to a server via a secure communication protocol.

[0872] Server means and generation means:

[0873] The server analyzes the received data and assesses the health status of pregnant women. Based on this analysis, a generation system generates nutritional and exercise advice for each pregnant woman. In addition to health indicators, the analysis also takes into account the pregnant woman's lifestyle and past health.

[0874] Emotional engine:

[0875] The server inputs the user's voice and text data into an emotion engine to evaluate the user's emotional state. Based on this evaluation, the system determines the pregnant woman's mental state and provides appropriate support and advice.

[0876] Means of notification:

[0877] The system notifies the user's smart device of advice and analysis results from the emotion engine, which have been adjusted by the generation method. This notification includes information tailored to the pregnant woman's current emotional and health status, providing the user with empathetic support.

[0878] AI methods and update methods:

[0879] AI-powered systems provide rapid responses to user inquiries. Furthermore, the servers regularly acquire the latest medical information and update the database, ensuring that advice is provided based on highly reliable data.

[0880] Specific example:

[0881] For example, if a pregnant woman is experiencing stress,

[0882] User: Uses a smartphone app to consult about recent fatigue via voice.

[0883] Server: Analyzes voice data using an emotion engine to confirm that the user is in a high-stress state.

[0884] Generation method: Adjusting special breathing techniques and relaxation advice for stress management.

[0885] Notification method: Tailored advice is immediately notified to the user's device.

[0886] User: By following the advice provided and working on stress management, I feel mentally better the next day.

[0887] Thus, the system of the present invention comprehensively supports the physical and mental anxieties that pregnant women experience, enabling them to spend their pregnancy with peace of mind.

[0888] The following describes the processing flow.

[0889] Step 1:

[0890] The device records the pregnant woman's physiological data. For example, it measures heart rate, activity level, and sleep patterns using sensors and creates a data log.

[0891] Step 2:

[0892] The device sends the collected data to the server. The data is transferred to the server via Wi-Fi or Bluetooth using a secure communication protocol.

[0893] Step 3:

[0894] The server receives physiological data transmitted from the terminal. The received data is input into the analysis engine, preparing it to evaluate the health status.

[0895] Step 4:

[0896] The server uses an analysis engine to assess health status. AI algorithms identify normal physiological changes and abnormalities, detecting anomalies in the data.

[0897] Step 5:

[0898] The server runs an emotion engine that analyzes the emotional state of the user's voice and text. By evaluating stress levels and emotional tendencies, it understands the user's mood.

[0899] Step 6:

[0900] The generation mechanism creates personalized advice based on health status and emotional assessments. It designs advice that includes specific guidelines for nutrition, exercise, and stress management.

[0901] Step 7:

[0902] The server sends the generated advice to the user via a notification system. Push notifications are sent to smartphones and tablets so that users can check them immediately.

[0903] Step 8:

[0904] Users submit questions and feedback via the app. They communicate their questions and thoughts about the system through text or voice input.

[0905] Step 9:

[0906] The AI ​​system prepares answers based on user inquiries. The server generates the optimal answer from past data and FAQs and sends it to the user.

[0907] Step 10:

[0908] The server periodically retrieves medical information using update mechanisms and updates the database. This ensures that the advice generated based on the latest medical information is always up-to-date.

[0909] (Example 2)

[0910] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0911] Pregnant women require comprehensive health support because their physical and mental states fluctuate significantly during pregnancy. However, conventional health management systems have struggled to provide comprehensive support that takes into account the emotional state and lifestyle of individual users. Therefore, providing appropriate support tailored to the individual needs of pregnant women remains a challenge.

[0912] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0913] In this invention, the server includes information gathering means, data transfer means, information processing means, instruction generation means, information notification means, and emotion evaluation means. This makes it possible to provide optimal nutrition and exercise advice to individual pregnant women based on physiological data, and furthermore, to provide comprehensive mental support that also takes into account their emotional state.

[0914] "Information gathering means" refers to a device or system for continuously collecting physiological data and activity-related information.

[0915] "Data transfer means" refers to a device or protocol that has the function of transmitting collected data to another system via a communication line.

[0916] "Information processing means" refers to a system or software for analyzing received data and evaluating an individual's health condition.

[0917] A "directive generation means" is a system or algorithm that derives the optimal nutrition and exercise plan for the user based on the analysis results.

[0918] An "information notification means" is a device or function that conveys generated instructions or advice to the user.

[0919] An "emotional assessment tool" is a system or tool for analyzing a user's emotional state and taking appropriate action based on that analysis.

[0920] A "knowledge description means" is a system or algorithm for generating appropriate answers to user inquiries.

[0921] A "data update mechanism" is a system that has the function of acquiring the latest health-related information and updating the database within the system.

[0922] The system in this invention is designed to comprehensively support the health status of pregnant women. The system achieves personalized health management through diverse data processing between terminals, servers, and users.

[0923] The system uses wearable devices and smartphones to continuously record the pregnant woman's heart rate, activity level, and sleep patterns. These devices use Bluetooth technology to collect data in real time. The collected data is transmitted to a server via Wi-Fi or a mobile network using a secure communication protocol (e.g., SSL / TLS).

[0924] The server analyzes the received data using analysis software (including Python libraries such as Pandas and NumPy). Based on the analysis results, a generative AI model (for example, the Transformers natural language processing library) generates nutrition and exercise advice. The server also analyzes voice and text data using an emotion assessment system and evaluates the user's emotional state using Google Cloud's Natural Language API and other tools.

[0925] The generated advice and sentiment assessment results are notified to the user's smart device via Firebase Cloud Messaging. Users receive the notifications and incorporate them into their daily lives to manage their health. For example, they might incorporate specific exercises or practice relaxation techniques according to stress management advice.

[0926] As a concrete example, if a user is experiencing stress, the following prompt message can be used.

[0927] "I've been feeling really tired lately, and I'd like some advice on how to reduce stress."

[0928] The server analyzes this prompt and uses a generative AI model to provide a highly relevant response. Users can then use the provided advice to work towards improving their mental health.

[0929] In this way, this system provides effective support to pregnant women, enabling them to have a safe and secure pregnancy through comprehensive health support.

[0930] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0931] Step 1:

[0932] The device collects physiological data such as heart rate, activity level, and sleep patterns from the pregnant woman's wearable device or smartphone. These devices use Bluetooth to collect data in real time. The input is physiological data from the sensors, and the output is a data packet containing this data. The device checks the integrity of the data and retries if there are missing values.

[0933] Step 2:

[0934] The device collects data and sends it to the server via Wi-Fi or a mobile network. The data is encrypted using SSL / TLS to ensure security. Input is data packets, and output is the data sent to the server. The device waits for an ACK response to confirm successful transmission and retransmits the data if necessary.

[0935] Step 3:

[0936] The server analyzes the received data. Using Python's Pandas and NumPy libraries, data preprocessing and analysis are performed to assess the health status of pregnant women. Input is physiological data sent from the terminal, and output is the analysis results. By sorting the data chronologically and performing statistical analysis, outliers and patterns are detected.

[0937] Step 4:

[0938] The server uses a generative AI model based on the analysis results to generate nutrition and exercise advice. Leveraging the Hugging Face Transformers library, the advice is generated in natural language. The input is analyzed health data, and the output is text-based advice. This generation process also references the latest information from medical databases.

[0939] Step 5:

[0940] The server inputs user voice and text data into an emotion assessment system and evaluates the emotional state. It uses Google Cloud's Natural Language API for voice analysis and calculates an emotion score. Input is user voice or text, and output is the emotion assessment result. The emotion model's accuracy is improved through machine learning.

[0941] Step 6:

[0942] The server sends the generated advice and sentiment evaluation results to the user's smart device using an information notification system. Notifications are sent in real time using Firebase Cloud Messaging. The input is the generated advice and sentiment evaluation results, and the output is a notification displayed on the user's device. The notification is formatted in a concise and easy-to-understand manner.

[0943] Step 7:

[0944] The user receives notifications and takes action to manage their health accordingly. This might involve performing specific exercises or eating meals based on nutritional advice. The input is the notifications received on the device, and the output is the improvement in the user's health status. The app records the progress of these efforts to help with future data collection.

[0945] Step 8:

[0946] The server processes user inquiries using knowledge description tools and quickly generates answers using an AI model. It utilizes OpenAI's large-scale language model to extract relevant information and generate appropriate responses. Input is the user's question, and output is the generated answer. The answers are reviewed by medical professionals to ensure reliability.

[0947] (Application Example 2)

[0948] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0949] For pregnant women to receive products and services that are better suited to their health and emotional state, effective and timely information sharing and accurate advice from specialized store staff are necessary. However, conventional systems have struggled to provide real-time responses tailored to the individual circumstances of pregnant women, resulting in a limitation to providing generic services.

[0950] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0951] In this invention, the server includes detection means for continuously collecting the physiological state of a pregnant woman, transmission means for transmitting the data acquired from the detection means via a communication medium, and creation means for generating individual health and behavioral guidelines in accordance with the pregnant woman's health status and emotional evaluation. This enables real-time analysis of the pregnant woman's health status and the provision of optimal products and services.

[0952] "Detection means" refers to a device or system that has the function of continuously collecting information on the physiological state of a pregnant woman.

[0953] "Transmission means" refers to a device or process that has the function of transmitting data acquired from detection means to a server via a communication medium.

[0954] A "processing means" is a system that has the capability to receive and analyze data sent by a transmission means.

[0955] "Generative means" refers to a device or process that has the function of generating individual health and behavioral guidelines based on the health status and emotional assessment of pregnant women.

[0956] "Notification means" refers to a device or process for informing the user of guidelines generated by the creation means.

[0957] "Distribution method" refers to a system that provides pregnant women with the most suitable products or services based on notification methods.

[0958] "Artificial intelligence means" refers to a system capable of generating optimal solutions based on the consultation content received from pregnant women.

[0959] A "new information acquisition means" is a device or system that has the function of acquiring the latest medical and health-related information and updating the data used in the creation means.

[0960] This invention is a system aimed at monitoring the health and emotional state of pregnant women in real time and providing them with the most suitable products and services. The specific implementation method is described below.

[0961] First, the user's device incorporates a detection mechanism that continuously collects physiological data such as the pregnant woman's heart rate and activity level. Smartwatches and fitness trackers are commonly used as sensor devices. The collected data is transmitted to a server via a secure communication protocol (e.g., HTTPS) using a transmission mechanism.

[0962] The server analyzes the received physiological data through processing devices to assess the pregnant woman's health. During this process, sentiment analysis can be performed using software such as the Google Cloud Natural Language API. Based on the analysis results, personalized health advice is generated by a creation device. This advice is instantly sent to the user's smartphone using a notification device.

[0963] Furthermore, store staff can use artificial intelligence to quickly respond to specific inquiries from users. For example, if a user inquires about the cause of their fatigue or stress, the AI ​​will compare it with past data and provide appropriate advice. Based on the generated health advice, a distribution system is used to deliver the most suitable products and services to the user.

[0964] This system also incorporates a means of acquiring new information, regularly obtaining the latest medical information and updating the database. This updated information is reflected in the datasets used in the creation process, forming the basis for providing the most up-to-date and reliable service.

[0965] As a concrete example, a user can notify the store of their health assessment via the app before visiting, allowing store staff to immediately suggest products and services based on that assessment upon arrival. An example of a prompt message could be, "Based on the pregnant woman's health data and sentiment analysis, please suggest suitable health products and mental care services."

[0966] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0967] Step 1:

[0968] The user's device continuously collects physiological data such as heart rate and activity level using wearable devices such as smartwatches. The input is the physiological data from the wearable device, and the output is this data being stored in the device's application.

[0969] Step 2:

[0970] The terminal transmits the collected physiological data to the server using a communication protocol (e.g., HTTPS). The input is the stored physiological data, and the output is the transmission and reception of this data to and from the server.

[0971] Step 3:

[0972] The server analyzes the received data using processing tools and performs a health assessment. The input is physiological data sent to the server, and the output is the health status assessment result based on this data. The server can also perform sentiment analysis using the Google Cloud Natural Language API.

[0973] Step 4:

[0974] The server generates personalized health advice based on the analyzed health assessment results. The input is the health assessment results, and the output is specific health advice for the user.

[0975] Step 5:

[0976] The server sends the generated advice to the user's smartphone via a notification system. The input is individual health advice, and the output is the notification of this advice to the user's device.

[0977] Step 6:

[0978] The user takes action based on the advice they receive, for example, deciding to visit a store. The user notifies the store of their status via their device. The input is the health advice received, and the output is the user's action based on it.

[0979] Step 7:

[0980] The server uses artificial intelligence to generate the optimal solution based on the user's inquiry. The input is the user's inquiry, and the output is the appropriate solution generated by the AI.

[0981] Step 8:

[0982] The server periodically acquires the latest medical information using new information acquisition methods and updates the database. The input is medical information acquired from external sources, and the output is an updated health information database.

[0983] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0984] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0985] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0986] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0987] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0988] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0989] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0990] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0991] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0992] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0993] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0994] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0995] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0996] 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.

[0997] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0998] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0999] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1000] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1001] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1002] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1003] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1004] The following is further disclosed regarding the embodiments described above.

[1005] (Claim 1)

[1006] A sensor means for continuously collecting physiological data of pregnant women,

[1007] A transmission means for transmitting data collected from the aforementioned sensor means via a communication network,

[1008] A server means for receiving and analyzing data transmitted from the aforementioned transmission means,

[1009] A means for generating individual nutrition and exercise advice tailored to the health condition of pregnant women,

[1010] A notification means for notifying the user of the advice generated by the generation means,

[1011] A system that includes this.

[1012] (Claim 2)

[1013] Equipped with an AI mechanism to receive inquiries from pregnant women and generate the most appropriate answers.

[1014] The system according to claim 1.

[1015] (Claim 3)

[1016] The system includes an update means for acquiring the latest medical information and updating the data used in the generation means.

[1017] The system according to claim 1.

[1018] "Example 1"

[1019] (Claim 1)

[1020] A detection method for collecting information on the menstrual cycle of pregnant women,

[1021] A transmission means for communicating information from the detection means,

[1022] Information processing means that receives information communicated from the aforementioned transmission means and performs analysis using an artificial intelligence algorithm,

[1023] A generation method that generates individual nutrition and exercise guidelines based on the health status of pregnant women, and utilizes a generation AI model to also consider the latest medical guidelines,

[1024] A notification means for transmitting the guidelines generated by the generation means to an information terminal,

[1025] A system that includes this.

[1026] (Claim 2)

[1027] The system according to claim 1, comprising artificial intelligence means for receiving questions from users and generating optimal responses using natural language processing technology.

[1028] (Claim 3)

[1029] The system according to claim 1, further comprising information updating means for acquiring the latest medical information from an external source and updating the data used in the generation means.

[1030] "Application Example 1"

[1031] (Claim 1)

[1032] A sensor means for continuously collecting physiological data of pregnant women,

[1033] A transmission means for transmitting data collected from the aforementioned sensor means via a communication network,

[1034] A server means for receiving and analyzing data transmitted from the aforementioned transmission means,

[1035] A means for generating individual nutrition and exercise advice tailored to the health condition of pregnant women,

[1036] A notification means for notifying the user of the advice generated by the generation means,

[1037] A display means that suggests products or services based on the health data of customers who visit the store,

[1038] A system that includes this.

[1039] (Claim 2)

[1040] The system according to claim 1, comprising an AI means for receiving inquiries from pregnant women and generating the most appropriate response.

[1041] (Claim 3)

[1042] The system according to claim 1, further comprising an update means for acquiring the latest medical information and updating the data used in the generation means.

[1043] "Example 2 of combining an emotion engine"

[1044] (Claim 1)

[1045] Information collection means for recording physiological data,

[1046] A data transfer means that transfers usage information from the aforementioned information collection means via a communication line,

[1047] Information processing means that receives and analyzes usage information transferred from the data transfer means,

[1048] Instruction generation means for creating individual nutrition and exercise plans,

[1049] Information notification means for transmitting instructions created by the instruction generation means to the user,

[1050] A means of emotional assessment for analyzing the emotional state of users,

[1051] A system that includes this.

[1052] (Claim 2)

[1053] The system according to claim 1, comprising a knowledge description means for receiving user inquiries and creating appropriate responses.

[1054] (Claim 3)

[1055] The system according to claim 1, further comprising a data update means for acquiring the latest health-related information and updating the information used by the instruction generation means.

[1056] "Application example 2 of combining emotional engines"

[1057] (Claim 1)

[1058] A detection means for continuously collecting information on the physiological state of pregnant women,

[1059] A transmission means for transmitting data acquired from the detection means via a communication medium,

[1060] A processing means for receiving and analyzing data sent from the aforementioned transmission means,

[1061] A means for generating individual health and behavioral guidelines based on the health status and emotional assessment of pregnant women,

[1062] A notification means for informing the user of the guidelines generated by the creation means,

[1063] A means of providing goods or services based on the aforementioned guidelines,

[1064] A system that includes this.

[1065] (Claim 2)

[1066] The system according to claim 1, which includes an artificial intelligence means for receiving inquiries from pregnant women and generating optimal solutions, and which allows store staff to refer to the health status in real time.

[1067] (Claim 3)

[1068] The system according to claim 1, comprising a new information acquisition means for acquiring the latest medical and health-related information and updating the data used in the creation means, thereby optimizing product proposals for users. [Explanation of Symbols]

[1069] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A sensor means for continuously collecting physiological data from pregnant women, A transmission means for transmitting data collected from the aforementioned sensor means via a communication network, A server means for receiving and analyzing data transmitted from the aforementioned transmission means, A means for generating individual nutrition and exercise advice tailored to the health condition of pregnant women, A notification means for notifying the user of the advice generated by the generation means, A system that includes this.

2. Equipped with an AI mechanism to receive inquiries from pregnant women and generate the most appropriate answers. The system according to claim 1.

3. The system includes an update means for acquiring the latest medical information and updating the data used in the generation means. The system according to claim 1.

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