System

Abstract

To provide a system.SOLUTION: A system comprises means for allowing a user to provide input information via a terminal, and a server that includes means for: receiving the input information and organizing the data; transmitting a prompt sentence relating to the input information to a generative AI model; causing the generative AI model to predict hormone fluctuations based on the input information; receiving data relating to the prediction of hormone fluctuations and organizing the data relating to the prediction of the hormone fluctuations; visualizing the data of prediction of hormone fluctuations organized by the organizing means, as a graph or a chart; and transmitting a visualization result visualized by the visualizing means to the user terminal, and the system comprising means for allowing the user to check the visualization result on the terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The challenges faced by women who suffer from PMS (premenstrual syndrome) are that it is difficult to gain understanding from those around them and that it is difficult for them to recognize that they are experiencing PMS. [Means for solving the problem]

[0005] The solution to this problem is a system that uses generative AI to visualize female hormone fluctuations. Specifically, the generative AI predicts hormone fluctuations based on information such as the user's menstrual cycle and changes in physical condition, and visualizes the results. Furthermore, the generative AI learns from user feedback to improve the accuracy of hormone fluctuation predictions. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is a female hormone fluctuation visualization system that uses generative AI. In this system, generative AI predicts hormone fluctuations based on information input by the user, such as information on menstrual cycles and changes in physical condition. The predicted results are presented to the user in the form of graphs or charts, visualizing hormone fluctuations and the accompanying changes in physical condition.

[0029] "Example 2"

[0030] Furthermore, the Generative AI learns from user feedback. Specifically, users input actual data on changes in their physical condition and menstrual cycle into the system, and the Generative AI updates the hormone fluctuation prediction model based on that data. This improves the accuracy of hormone fluctuation predictions, making it possible to more accurately predict changes in each user's physical condition.

[0031] "Example 3"

[0032] In another embodiment of the present invention, a female hormone fluctuation visualization system using generative AI is implemented as a smartphone application. The user inputs information about their menstrual cycle and changes in their physical condition via their smartphone, and the generative AI predicts hormone fluctuations based on this information. The predicted results are visualized on the smartphone screen, allowing the user to check their own hormone fluctuations at any time.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user enters information about their menstrual cycle and physical condition into the system.

[0036] Step 2: Generative AI predicts hormone fluctuations based on input information.

[0037] Step 3: Present the prediction results to the user in the form of graphs, charts, etc., to visualize hormone fluctuations and the resulting changes in physical condition.

[0038] "Example 2"

[0039] Step 1: The user enters actual data about their physical condition and menstrual cycle into the system.

[0040] Step 2: The generative AI updates the hormone fluctuation prediction model based on the input data.

[0041] Step 3: Using the updated prediction model, the accuracy of predicting hormone fluctuations is improved, allowing for more accurate prediction of changes in each user's physical condition.

[0042] "Example 3"

[0043] Step 1: The user inputs information about their menstrual cycle and changes in physical condition via their smartphone. Step 2: The generative AI predicts hormone fluctuations based on this information.

[0044] Step 3: The prediction results are visualized on the smartphone screen, allowing users to check their own hormone fluctuations at any time.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0047] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), significantly impacting the quality of women's lives. However, there is a lack of means to accurately predict and visualize these hormone fluctuations and the accompanying changes in physical condition, making it difficult for women themselves to understand their own changes in physical condition, and for those around them to understand them. Furthermore, existing systems face the challenge of making it difficult to effectively utilize user feedback to improve prediction accuracy.

[0048] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0049] In this invention, the server includes: a means for the user to provide input information through a terminal; a means for the server to receive the input information and organize the data; a means for the server to send prompts to the generative AI model; a means for the generative AI model to predict hormone fluctuations; a means for the server to receive the prediction results and organize the data; a means for the server to visualize the prediction results as graphs or charts; a means for the server to send the visualization results to the user's terminal; and a means for the user to check the visualization results on the terminal. This allows the user to visually understand their own hormone fluctuations and the accompanying changes in their physical condition, making it easier for others to understand. Furthermore, the generative AI model can improve the accuracy of hormone fluctuation predictions by learning from user feedback.

[0050] "User" refers to an individual who uses the system to provide information about their menstrual cycle and changes in physical condition.

[0051] "Device" refers to the electronic device used by the user to input information and check the results. Specifically, this includes smartphones and personal computers.

[0052] "Server" refers to the computer system responsible for receiving input information from users, processing the data, and sending it to the generative AI model.

[0053] "Generative AI model" refers to an artificial intelligence model that predicts hormone fluctuations based on data provided by the user.

[0054] "Prompt sentence" refers to an instruction sent to a generative AI model to make a prediction.

[0055] "Hormonal fluctuations" refers to the fluctuations in hormone levels, such as estrogen and progesterone, that occur during a woman's menstrual cycle.

[0056] "Organizing data" refers to converting user-provided information into an appropriate format that makes it easier to use for analysis and prediction.

[0057] "Visualization" refers to displaying the predicted hormone fluctuation results in a visual format such as a graph or chart.

[0058] "Feedback" refers to additional or corrective information provided by users that is used to improve the predictive accuracy of generative AI models.

[0059] This invention is a system for visualizing female hormone fluctuations using a generative AI model. Specific embodiments of this system are described below.

[0060] First, the user provides information about their menstrual cycle and changes in their physical condition through a device. The device can be a common electronic device such as a smartphone or PC. The user enters the following data through a dedicated application or web form:

[0061] Menstrual cycle data: [28, 30, 27, 29]

[0062] Physical condition change data: ["headache", "fatigue", "depression"]

[0063] The server then receives the data sent by the user and processes it using the Python Pandas library to organize the data into a data frame, which converts the data into a format that is easy to use for analysis and prediction.

[0064] The server sends the organized data to a generative AI model. The generative AI model uses an advanced artificial intelligence model such as OpenAI's GPT-4. The server generates a prompt like the following and sends it to the generative AI model:

[0065] User's menstrual cycle data: [28, 30, 27, 29]

[0066] User's physical condition data: ["headache", "fatigue", "depression"]

[0067] Use this data to predict hormone fluctuations during your next menstrual cycle.

[0068] The generative AI model predicts hormone fluctuations based on the prompt text sent, generating predictions such as the following:

[0069] The hormonal fluctuations during your next menstrual cycle are as follows:

[0070] Day 1: Estrogen levels drop

[0071] Day 5: Progesterone levels rise

[0072] Day 14: Estrogen levels peak

[0073] Day 21: Progesterone levels peak

[0074] The server receives the prediction results returned by the generative AI model and organizes them as a data frame using Pandas. The organized prediction results are then visualized as graphs and charts using the Matplotlib library. For example, a line chart showing the fluctuations in estrogen and progesterone is generated.

[0075] Finally, the server sends the generated graphs and charts to the user's device, where the user can view them and visually understand the hormonal fluctuations and accompanying physical changes during the next menstrual cycle.

[0076] This system allows users to accurately understand their own hormone fluctuations and manage their physical condition. In addition, the generative AI model can improve the accuracy of hormone fluctuation predictions by learning from user feedback.

[0077] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0078] Step 1:

[0079] The user provides input information through the terminal.

[0080] Specifically, users use a smartphone or computer to enter information about their menstrual cycle and changes in their physical condition into a dedicated application or web form.

[0081] An example of the data to be entered is as follows:

[0082] Menstrual cycle data: [28, 30, 27, 29]

[0083] Physical condition change data: ["headache", "fatigue", "depression"]

[0084] Input: Menstrual cycle data and physical condition change data entered by the user into the device

[0085] Output: Input data sent from the device to the server

[0086] Step 2:

[0087] The server receives the input and organizes the data.

[0088] The server receives the data submitted by the user and organizes it into a data frame using Python's Pandas library.

[0089] Specifically, the data is organized as follows:

[0090] Convert menstrual cycle data and physical condition change data into a data frame

[0091] Input: Menstrual cycle data and physical condition change data sent from the device

[0092] Output: A neat data frame

[0093] Step 3:

[0094] The server sends a prompt to the generative AI model.

[0095] The server generates prompt sentences based on the organized data and sends them to the generative AI model.

[0096] An example prompt is:

[0097] User's menstrual cycle data: [28, 30, 27, 29]

[0098] User's physical condition data: ["headache", "fatigue", "depression"]

[0099] Use this data to predict hormone fluctuations during your next menstrual cycle.

[0100] Input: A cleaned data frame

[0101] Output: The prompt sent to the generative AI model

[0102] Step 4:

[0103] Generative AI models predict hormone fluctuations.

[0104] A generative AI model (e.g., GPT-4) predicts hormone fluctuations based on the prompt text sent.

[0105] Specifically, it generates the following predictions:

[0106] The hormonal fluctuations during your next menstrual cycle are as follows:

[0107] Day 1: Estrogen levels drop

[0108] Day 5: Progesterone levels rise

[0109] Day 14: Estrogen levels peak

[0110] Day 21: Progesterone levels peak

[0111] Input: The prompt sent to the generative AI model

[0112] Output: The predicted results returned by the generative AI model

[0113] Step 5:

[0114] The server receives the prediction results and organizes the data.

[0115] The server receives the prediction results returned by the generative AI model and organizes them as a data frame again using Pandas.

[0116] Specifically, the data is organized as follows:

[0117] Convert the prediction results into a data frame

[0118] Input: Prediction results returned by the generative AI model

[0119] Output: A data frame of organized prediction results

[0120] Step 6:

[0121] The server visualizes the prediction results as graphs and charts.

[0122] The server visualizes the organized prediction results as graphs and charts using the Matplotlib library.

[0123] Specifically, the graph is generated as follows:

[0124] Create a line chart showing fluctuations in estrogen and progesterone

[0125] Input: Data frame of organized prediction results

[0126] Output: Generated graphs and charts

[0127] Step 7:

[0128] The server sends the visualization results to the user's terminal.

[0129] The server sends the generated graphs and charts to the user's terminal.

[0130] Specifically, the data may be sent using an HTTP response or through a dedicated application.

[0131] Input: Generated graphs and charts

[0132] Output: Visualization results sent to the user's device

[0133] Step 8:

[0134] The user checks the visualization results on the device.

[0135] Users can check the graphs and charts received on their device to visually understand the hormonal fluctuations and accompanying changes in physical condition during their next menstrual cycle.

[0136] Specifically, it displays graphs and charts through an application or web browser.

[0137] Input: Visualization results sent from the server

[0138] Output: Graphs and charts that users can view on their devices

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS) and have a significant impact on women's quality of life. However, these hormone fluctuations and the accompanying changes in physical condition are difficult for those around them to understand, and it is also difficult for women to recognize them themselves. Furthermore, there are challenges in receiving appropriate health management and product recommendations.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for predicting hormone fluctuations based on information such as the user's menstrual cycle and changes in physical condition and visualizing the results, and a means for providing health care advice and product recommendations suited to the user. As a result, by accurately predicting and visualizing female hormone fluctuations, the user can more easily understand changes in their own physical condition and receive appropriate health care and product recommendations.

[0144] "Female hormone fluctuations" are changes in the levels of hormones such as estrogen and progesterone that fluctuate periodically in a woman's body.

[0145] "Visualization" refers to the display of data or information in a visual format such as a graph or chart.

[0146] "Generative AI" is an artificial intelligence technology that makes predictions and generates information based on input from users.

[0147] The "menstrual cycle" refers to the period from the first day of a woman's menstrual period to the first day of her next menstrual period.

[0148] "Changes in physical condition" refers to any physical or mental changes or symptoms experienced by the user.

[0149] "Health Management Advice" means specific suggestions or instructions to improve or maintain a user's health.

[0150] "Product suggestion" refers to recommending appropriate products based on the user's needs and condition.

[0151] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before their period.

[0152] "Feedback" refers to opinions and reactions provided by users, and is information used to improve the performance of a system.

[0153] The system for implementing this invention visualizes female hormone fluctuations and uses a generation AI to provide health management advice and product recommendations tailored to the user. Specific embodiments of this system are described below.

[0154] System configuration

[0155] The system consists of the following main components:

[0156] 1. User device: The user enters information about their menstrual cycle and changes in physical condition on a device such as a smartphone or tablet.

[0157] 2. Server: Runs the generative AI model, predicts hormone fluctuations based on user input, and visualizes the results.

[0158] 3. Generative AI model: Predicts hormone fluctuations based on user input and generates health management advice and product suggestions.

[0159] Program processing

[0160] 1. On the user's device, enter:

[0161] Users use a smartphone or tablet to enter information such as the start date of their menstrual cycle, the length of the cycle, and changes in their physical condition (e.g., fatigue, abdominal pain).

[0162] 2. Data processing on the server:

[0163] The server receives the data sent by the user and inputs it into a generative AI model, which predicts hormone fluctuations based on the user's menstrual cycle and changes in physical condition.

[0164] 3. Prediction and visualization of hormone fluctuations:

[0165] The generative AI model visualizes the predicted hormone fluctuations as graphs and charts, allowing users to visually understand their own hormone fluctuations.

[0166] 4. Health care advice and product suggestions:

[0167] The generative AI model uses predicted hormone fluctuations and information on changes in the user's physical condition to generate appropriate health management advice and product recommendations, such as suggesting supplements or fitness programs needed at specific times.

[0168] Hardware and software used

[0169] Hardware: smartphones, tablets, servers

[0170] Software: Generative AI models, data processing libraries (e.g., Python, Matplotlib)

[0171] Specific examples

[0172] The user uses their smartphone to enter the following information:

[0173] Menstrual cycle start date: October 1, 2023

[0174] Cycle length: 28 days

[0175] Changes in physical condition: fatigue, abdominal pain

[0176] Using this information, the generative AI model predicts hormone fluctuations over the next 28 days and generates prompts like this:

[0177] The user's menstrual cycle starts on October 1, 2023, and is 28 days long. The user is experiencing fatigue and abdominal pain. Based on this information, predict hormone fluctuations for the next 28 days and provide appropriate health management advice.

[0178] The generative AI model predicts hormone fluctuations based on the prompt text and visualizes them as graphs. It also provides users with appropriate health management advice and product recommendations, such as suggesting supplements or fitness programs needed at specific times.

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The user uses a smartphone or tablet to input information such as the start date of the menstrual cycle, the length of the cycle, and changes in physical condition (e.g., fatigue, abdominal pain).

[0182] Input: menstrual cycle start date, cycle length, and information on changes in physical condition

[0183] Output: User input data

[0184] Specific operation: The user enters the required information into the application's input form and presses the submit button.

[0185] Step 2:

[0186] The terminal transmits the user's input data to the server.

[0187] Input: User-entered data

[0188] Output: User data sent to the server

[0189] Specific operation: The terminal sends the input data to the server as an HTTP request.

[0190] Step 3:

[0191] The server receives the data sent by the user and converts it into a data format for input into the generative AI model.

[0192] Input: User data sent to the server

[0193] Output: Data format that can be input to a generative AI model

[0194] Specific operation: The server parses the received data and converts it into the required format (e.g., JSON format).

[0195] Step 4:

[0196] The server uses the generated AI model to predict hormone fluctuations based on information about the user's menstrual cycle and changes in physical condition.

[0197] Input: Data format that can be input to the generative AI model

[0198] Output: Predicted hormone fluctuation data

[0199] Specific operation: The server inputs data into the generative AI model and obtains predicted results for hormone fluctuations.

[0200] Step 5:

[0201] The server visualizes the predicted hormone fluctuation data as graphs and charts.

[0202] Input: Predicted hormone fluctuation data

[0203] Output: Visualized graphs and charts

[0204] What it does: The server uses libraries such as Matplotlib to convert hormone fluctuation data into graphs and charts.

[0205] Step 6:

[0206] The server uses the generated AI model to generate health management advice and product suggestions appropriate for the user.

[0207] Input: Predicted hormone fluctuation data, user's physical condition change information

[0208] Output: Health care advice and product suggestions

[0209] Specific operation: The server inputs data into the generative AI model to generate health management advice and product suggestions.

[0210] Step 7:

[0211] The server transmits visualized graphs and charts, as well as health management advice and product suggestions, to the user's terminal.

[0212] Input: visualized graphs and charts, health management advice and product suggestions

[0213] Output: Data sent to the user's terminal

[0214] Specific operation: The server sends the generated data to the user terminal as an HTTP response.

[0215] Step 8:

[0216] The user terminal receives the data sent from the server and displays it on the application.

[0217] Input: Data sent from the server

[0218] Output: Graphs and charts displayed on the application, health management advice and product suggestions

[0219] Specific operation: The user device analyzes the received data and displays it on the application's UI.

[0220] Example 2

[0221] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0222] Fluctuations in female hormones cause symptoms such as PMS (premenstrual syndrome), placing a significant burden on women. However, these symptoms are often difficult to understand by those around them, and women themselves often find it difficult to recognize them as PMS. Conventional systems have low accuracy in predicting hormone fluctuations based on individual users' physical changes and menstrual cycles, making it difficult to take appropriate measures. Therefore, there is a need for a system that can accurately predict hormone fluctuations based on a user's physical changes and menstrual cycles, and visualize the results.

[0223] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0224] In this invention, the server includes means for the user to input data regarding changes in physical condition and menstrual cycle, means for transmitting the data to the server, means for storing the data, means for providing the data to a generative AI model, means for the generative AI model to predict hormone fluctuations based on the data and visualize the results, and means for the generative AI model to learn from feedback from the user and improve the accuracy of hormone fluctuation predictions.This makes it possible to solve the problem that women who suffer from PMS have difficulty getting understanding from those around them and also have difficulty recognizing that they are suffering from PMS themselves.

[0225] "User" refers to an individual who uses the system to input data regarding changes in physical condition and menstrual cycle.

[0226] "Changes in physical condition" refers to fluctuations in the user's physical or mental state, and specifically includes the start date of menstruation, body temperature, mood changes, etc.

[0227] "Menstrual cycle" refers to a woman's menstrual cycle and includes the period from the first day of menstruation to the first day of the next menstruation.

[0228] "Data" refers to information entered by the user regarding changes in physical condition and menstrual cycle.

[0229] "Terminal" refers to a device used by a user to input data, and specifically includes smartphones and personal computers.

[0230] "Server" refers to the computer system that receives, stores, and provides user-submitted data to the generative AI model.

[0231] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to learn from user data and predict hormone fluctuations.

[0232] "Hormonal fluctuations" refers to fluctuations in hormone levels that occur in a woman's body, particularly those associated with the menstrual cycle.

[0233] "Prediction" refers to the generative AI model estimating future hormone fluctuations based on user data.

[0234] "Visualization" refers to displaying the results of hormone fluctuations predicted by the generative AI model in a way that is easy for users to understand.

[0235] "Feedback" refers to additional data or opinions that users provide to the system, which are used to train the generative AI model.

[0236] "PMS (premenstrual syndrome)" refers to the physical and mental symptoms women experience before their period, including abdominal pain, headaches, and mood swings.

[0237] This invention is a system in which a user inputs data on changes in physical condition and menstrual cycle, and a generative AI model predicts hormone fluctuations based on that data and visualizes the prediction results. Specific embodiments of this system are described below.

[0238] Hardware and Software Configuration

[0239] Terminal

[0240] Users enter data using devices such as smartphones or computers. A dedicated application or website is installed on the device, and users enter data on changes in their physical condition and menstrual cycle through this.

[0241] server

[0242] The server receives and stores data sent by users. The server has a database and organizes and stores data for each user. The server also provides data to the generative AI model.

[0243] Generative AI Models

[0244] The generative AI model predicts hormone fluctuations based on data provided by the server, and uses machine learning algorithms to learn from user feedback and improve prediction accuracy.

[0245] Data processing and calculation

[0246] Entering and Submitting Data

[0247] The user enters data about their physical condition and menstrual cycle through the device. For example, the user might enter "My period started on October 1, 2023." The device then encrypts this data and sends it to the server using the HTTPS protocol.

[0248] Data storage

[0249] The server stores the received data in a database. For example, it stores it in the format "User ID: 12345, Menstruation Start Date: October 1, 2023".

[0250] Data provision

[0251] The server retrieves the data for "User ID: 12345" from the database and provides it to the generative AI model.

[0252] Model Updates and Predictions

[0253] The generative AI model updates the neural network based on the provided data and adjusts the parameters for predicting the start date of the next period. For example, the generative AI model learns from "User ID: 12345's menstrual start data on October 1, 2023" and more accurately predicts the start date of the next period.

[0254] Providing prediction results

[0255] The generative AI model generates a prediction result and returns it to the server in the format "User ID: 12345, Next Menstrual Start Date: October 29, 2023." The server then sends this result to the device, where the user can check it in the "Prediction Results" section of the app.

[0256] Specific examples

[0257] For example, if a user types, "My period started on October 1, 2023. Please predict when my next period will start," the server receives this information and provides it to the generative AI model, which then learns from this new data and more accurately predicts when her next period will start.

[0258] This system allows users to more accurately understand changes in their physical condition and take appropriate measures.

[0259] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0260] Step 1:

[0261] Users input data about changes in their physical condition and menstrual cycle. Specifically, they use a dedicated application or website on their smartphone or computer. For example, a user might enter, "My period started on October 1, 2023." The input data is temporarily stored on the device.

[0262] Step 2:

[0263] The device sends the data entered by the user to the server. The data is encrypted and securely sent to the server. For example, the device encrypts the data "My period started on October 1, 2023" and sends it to the server using the HTTPS protocol. The input is the user's physical condition data, and the output is encrypted data.

[0264] Step 3:

[0265] The server stores the received data in a database. The stored data is organized by user and can be accessed later by the generative AI model. For example, the server stores the data in the database in the format "User ID: 12345, Menstruation Start Date: October 1, 2023". The input is encrypted data, and the output is the data in the format stored in the database.

[0266] Step 4:

[0267] The server provides the stored data to the generative AI model, which then updates the hormone fluctuation prediction model based on this data. For example, the server provides the generative AI model with "User ID: 12345's menstrual start data on October 1, 2023." The input is the data retrieved from the database, and the output is the data provided to the generative AI model.

[0268] Step 5:

[0269] The generative AI model learns from the provided data and updates the hormone fluctuation prediction model. Specifically, it uses a machine learning algorithm to learn new data and improve prediction accuracy. For example, the generative AI model learns from "User ID: 12345's menstrual start data on October 1, 2023" and more accurately predicts the start date of the next menstrual period. The input is the provided data, and the output is an updated prediction model.

[0270] Step 6:

[0271] The generative AI model provides prediction results to the user based on the updated prediction model. The user can check the prediction results through their device. For example, the generative AI model predicts that "User ID: 12345's next menstrual start date is October 29, 2023" and returns this result to the server. The server sends this result to the device, and the user can check it in the "Prediction Results" section of the app. The input is the updated prediction model, and the output is the prediction results provided to the user.

[0272] (Application example 2)

[0273] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0274] Until now, there has been no system that can accurately predict fluctuations in female hormones and the accompanying changes in physical condition, and provide appropriate services based on that prediction. As a result, when women suffer from premenstrual syndrome (PMS), it is difficult for those around them to understand, and it is also difficult for them to recognize it themselves. There has also been a lack of means to provide appropriate electronic payment services in response to changes in physical condition.

[0275] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, and a means for the generation AI to predict hormone fluctuations based on data on the user's physical condition changes and menstrual cycle, and for providing electronic payment services based on the prediction results. This makes it possible to provide optimal electronic payment services in response to changes in the user's physical condition, making it easier for women suffering from PMS to gain understanding from those around them and to recognize the symptoms of PMS themselves.

[0276] A "means for visualizing female hormone fluctuations" is a device or software for visually displaying fluctuations in a user's hormone levels.

[0277] "Means using generative AI" refers to devices or software that use artificial intelligence techniques to analyze data and generate predictive models.

[0278] "Data on changes in the user's physical condition and menstrual cycle" refers to information entered by the user regarding changes in physical condition and menstrual cycle.

[0279] A "means for predicting hormone fluctuations" is a device or software that predicts future fluctuations in hormone levels based on data on changes in the user's physical condition and menstrual cycle.

[0280] "Means for providing electronic payment services" refers to devices or software for providing services related to electronic payments, such as cashback or discount coupons, based on the results of predictions of the user's physical condition and hormone fluctuations.

[0281] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before their period.

[0282] A system for implementing the present invention has the following configuration.

[0283] System configuration

[0284] 1. Hardware

[0285] Server: Stores data and runs generative AI models.

[0286] Terminal: The device (smartphone, tablet, etc.) where users enter data and view results.

[0287] 2. Software

[0288] Generative AI model: A machine learning model that predicts hormone fluctuations based on data on the user's physical condition and menstrual cycle.

[0289] Database: A system for storing user physical condition data and menstrual cycle data.

[0290] Electronic payment service: An application that provides cashback and discount coupons based on the user's physical condition.

[0291] Data processing and calculation

[0292] 1. Data Acquisition

[0293] Users use the device to input data about their physical condition and menstrual cycle, including a physical condition score (1-5) and hormone level score (0-1).

[0294] 2. Data Preprocessing

[0295] The server converts the input data into a numerical format that can be processed by the machine learning model. Specifically, it converts date data into numbers and converts physical condition scores and hormone levels into NumPy arrays.

[0296] 3. Model Update

[0297] The server uses a generative AI model to learn the relationship between physical condition scores and hormone levels, improving the accuracy of hormone fluctuation predictions.

[0298] 4. Prediction

[0299] When a new physical condition score is entered, the server uses a generative AI model to predict hormone levels.

[0300] 5. Offering

[0301] Based on the predicted hormone levels, the server provides users with electronic payment services such as cashback and discount coupons.

[0302] Specific examples

[0303] User A: If the health score is 4 (poor) and the predicted hormone level is 0.4, offer cashback.

[0304] User B: If the health score is 2 (good) and the predicted hormone level is 0.7, offer a discount coupon.

[0305] Prompt Sentence Examples

[0306] "If the user's health score is 4, offer cashback because the predicted hormone level is 0.4."

[0307] In this way, a system can be created in which generative AI predicts hormone fluctuations based on the user's physical condition data and provides optimal electronic payment services.

[0308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0309] Step 1:

[0310] The user uses the device to input data on changes in physical condition and menstrual cycle. Specifically, the user inputs a physical condition score (1-5) and a hormone level score (0-1). The input data is sent to the server.

[0311] Step 2:

[0312] The server converts the received data into a numerical format and formats it so that the machine learning model can process it. Specifically, it converts date data into numbers and converts the physical condition score and hormone levels into NumPy arrays. The input is the physical condition data from the user, and the output is the formatted data.

[0313] Step 3:

[0314] The server uses a generative AI model to learn the relationship between physical condition scores and hormone levels. Specifically, it uses a Linear Regression model to analyze the relationship between physical condition scores and hormone levels. The input is the formatted data, and the output is the trained model.

[0315] Step 4:

[0316] When a user inputs a new physical condition score, the server uses a generative AI model to predict hormone levels. Specifically, the server inputs the new physical condition score into the model and outputs the predicted hormone levels. The input is the new physical condition score, and the output is the predicted hormone levels.

[0317] Step 5:

[0318] The server provides users with electronic payment services such as cashback or discount coupons based on the predicted hormone levels. Specifically, if the predicted hormone levels are below a certain threshold, the server provides cashback, and if they are above a certain threshold, the server provides discount coupons. The input is the predicted hormone levels, and the output is the electronic payment service provided.

[0319] Example 3

[0320] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0321] Fluctuations in female hormones can cause symptoms such as premenstrual syndrome (PMS), which can have a significant impact on a woman's quality of life. However, there is a lack of means to accurately predict and visualize these hormone fluctuations, making it difficult for women to understand changes in their own physical condition and take appropriate measures. There are also problems with gaining understanding from those around them. This has led to a demand for solutions to women's problems and improving their quality of life.

[0322] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0323] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generating AI, a means for the user to input information about their menstrual cycle and changes in physical condition via a smartphone, a means for the terminal to transmit the input data to the server, a means for the server to input the received data into a generating AI model, a means for the generating AI model to predict hormone fluctuations, a means for the server to return the prediction results to the terminal, a means for the terminal to visualize the prediction results on the smartphone screen, and a means for the user to confirm the predicted hormone fluctuation results. This allows women to accurately understand their own hormone fluctuations and take appropriate measures. It also makes it easier for them to gain understanding from those around them, which is expected to improve their quality of life.

[0324] A "means for visualizing female hormone fluctuations" is a method or device for visually displaying fluctuations in hormone levels occurring in a woman's body.

[0325] "Means using generative AI" refers to methods or devices that use artificial intelligence technology to analyze data and make predictions or generate results.

[0326] "Means for users to input information about their menstrual cycle and changes in physical condition via smartphone" refers to a method or device that allows users to input data about their menstrual cycle and changes in physical condition using a smartphone application.

[0327] "Means for transmitting data entered by a terminal to a server" refers to a method or device for a terminal such as a smartphone to transmit data entered by a user to a server via the Internet.

[0328] "Means for inputting data received by the server into the generative AI model" refers to a method or device by which the server passes data received from the terminal to the generative AI model for analysis and prediction.

[0329] "Means for a generative AI model to predict hormone fluctuations" refers to a method or device for a generative AI model to predict fluctuations in a woman's hormone levels based on input data.

[0330] "Means for the server to return prediction results to the terminal" refers to a method or device by which the server transmits prediction results obtained by the generative AI model to the terminal.

[0331] "Means for the terminal to visualize the prediction results on the smartphone screen" refers to a method or device for the terminal to display the prediction results received from the server as graphs or charts on the smartphone screen.

[0332] "Means for users to check the predicted results of hormone fluctuations" refers to a method or device that allows users to check the predicted results of hormone fluctuations on the screen of their smartphone.

[0333] This invention is a system for visualizing female hormone fluctuations, which uses a generative AI model to predict hormone fluctuations based on information on a user's menstrual cycle and physical condition changes, and visualizes the results. Specific embodiments of this system are described below.

[0334] Hardware and Software Configuration

[0335] server

[0336] The server provides the computing resources to run the generative AI model. Specifically, an advanced natural language processing model such as OpenAI's GPT-4 or Google's BERT is used as the generative AI model. The server receives data sent by users and inputs it into the generative AI model.

[0337] Device (smartphone)

[0338] The device provides an interface for users to input information about their menstrual cycle and changes in their physical condition. Users input data through a smartphone application, which is then sent to a server via the Internet. The device also visualizes the prediction results received from the server and displays them to the user.

[0339] user

[0340] Users use a smartphone application to input information about their menstrual cycle and changes in physical condition, and the generative AI model then predicts hormone fluctuations and provides the results to the user.

[0341] Data processing and calculation

[0342] Data Entry

[0343] Users enter information such as the start and end dates of their menstrual cycle, body temperature, and mood swings through a smartphone application. For example, they can enter data such as "Menstruation start date: October 1, 2023," "Body temperature: 36.8 degrees," and "Mood: Unstable."

[0344] Data transmission

[0345] The terminal encrypts the input data and sends it to a server via the Internet, thereby maintaining data security.

[0346] Data Analysis and Prediction

[0347] The server inputs the received data into a generative AI model, which then predicts hormone fluctuations based on the input data. For example, it generates prompts like the following:

[0348] "The user's menstrual cycle started on October 1, 2023, and ended on October 7, 2023. Their body temperature fluctuated from 36.5°C to 37.0°C, and their mood changed from unstable to stable. Please use this information to predict their next menstrual cycle and hormone fluctuations."

[0349] Returning and visualizing prediction results

[0350] The server receives the results of the generative AI model's predictions of hormone fluctuations and sends them back to the device. The device then visualizes the received predictions as graphs and charts on the smartphone screen, allowing the user to visually confirm the predicted results of hormone fluctuations.

[0351] Specific examples

[0352] For example, if a user inputs "Period start date: October 1, 2023," "Body temperature: 36.8°C," and "Mood: Unstable," the device sends this data to the server. The server generates a prompt for the generative AI model: "The user's menstrual cycle started on October 1, 2023, and ended on October 7, 2023. Body temperature fluctuated from 36.5°C to 37.0°C, and mood changed from unstable to stable. Based on this information, please predict the next menstrual cycle and hormone fluctuations." This is then input into the model. The generative AI model predicts the start date of the next menstrual cycle and hormone level fluctuations and sends the results back to the server. The server then sends the prediction results back to the device, which visualizes them on the smartphone screen. The user can check the predicted start date of the next menstrual cycle and hormone fluctuations on the screen.

[0353] In this way, the user can accurately understand their own hormone fluctuations and take appropriate measures. In addition, it is easier to gain understanding from those around them, which is expected to improve the quality of life. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0354] Step 1:

[0355] The user launches the smartphone application.

[0356] Specific operation: A user launches the app by tapping the application icon on the smartphone home screen. When the app launches, the login screen is displayed.

[0357] Input: User taps.

[0358] Output: The application login screen is displayed.

[0359] Step 2:

[0360] The user inputs information about their menstrual cycle and changes in physical condition.

[0361] Specific operation: After logging in, the user enters information such as the start and end dates of their menstrual cycle, body temperature, and mood fluctuations into an input form within the application. For example, they might enter data such as "Menstruation start date: October 1, 2023," "Body temperature: 36.8 degrees," and "Mood: Unstable."

[0362] Input: Information about menstrual cycle and changes in physical condition entered by the user.

[0363] Output: The input data is saved in the application.

[0364] Step 3:

[0365] The terminal transmits the input data to the server.

[0366] Specific operation: The terminal encrypts the data entered by the user and sends it to a server via the Internet.

[0367] Input: Information about menstrual cycle and changes in physical condition entered by the user.

[0368] Output: The encrypted data is sent to the server.

[0369] Step 4:

[0370] The server inputs the received data into a generative AI model.

[0371] Specific operation: The server analyzes the data received from the device and inputs it into a generative AI model, such as OpenAI's GPT-4. This model generates prompts to predict hormone fluctuations based on the input data.

[0372] Input: Encrypted data sent from the terminal.

[0373] Output: The prompt sentence that is fed into the generative AI model.

[0374] Step 5:

[0375] Generative AI models predict hormone fluctuations.

[0376] How it works: The generative AI model predicts hormone fluctuations based on a prompt. For example, it might generate a prompt like this: "The user's menstrual cycle starts on October 1, 2023, and ends on October 7, 2023. Her body temperature fluctuated from 36.5°C to 37.0°C, and her mood changed from unstable to stable. Based on this information, please predict her next menstrual cycle and hormone fluctuations."

[0377] Input: The generated prompt statement.

[0378] Output: Predicted hormone fluctuations.

[0379] Step 6:

[0380] The server returns the prediction results to the device.

[0381] Specific operation: The server receives the predicted hormone fluctuation results obtained from the generative AI model and sends them back to the terminal.

[0382] Input: Prediction results from a generative AI model.

[0383] Output: The prediction results are sent to the device.

[0384] Step 7:

[0385] The device visualizes the prediction results on the smartphone screen.

[0386] Specific operation: The device visualizes the received prediction results as graphs and charts on the smartphone screen, allowing users to visually check the predicted results of hormone fluctuations.

[0387] Input: Prediction results sent from the server.

[0388] Output: Graphs and charts displayed on the smartphone screen.

[0389] Step 8:

[0390] The user checks the predicted results of hormone fluctuations.

[0391] How it works: Users can check the predicted hormone fluctuations on their smartphone screen, which will help them predict their next menstrual cycle and changes in their physical condition, and take appropriate measures.

[0392] Input: Prediction results displayed on a smartphone screen.

[0393] Output: Predicted hormone fluctuations confirmed by the user.

[0394] (Application example 3)

[0395] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0396] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), significantly affecting the quality of women's lives. However, it is difficult to accurately grasp these hormone fluctuations and take appropriate measures. There are also problems with gaining understanding from those around you, and with women finding it difficult to recognize PMS themselves. Furthermore, there is a lack of means to provide advice on health management and suggestions for appropriate exercise and diet. To solve these issues, a system is needed that visualizes hormone fluctuations and provides appropriate advice to users.

[0397] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0398] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generating AI, a means for the user to input information on menstrual cycles and changes in physical condition, a means for the generating AI to predict hormone fluctuations based on this information, a means for visualizing the prediction results, and a means for providing the user with health management advice and appropriate exercise and diet suggestions. This allows the user to accurately understand their own hormone fluctuations and take appropriate measures. It also makes it easier for them to gain understanding from those around them and to recognize PMS themselves. Furthermore, they can receive health management advice and appropriate exercise and diet suggestions.

[0399] "Female hormone fluctuations" refers to the changes in hormones such as estrogen and progesterone that fluctuate periodically within a woman's body.

[0400] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[0401] The "menstrual cycle" refers to the period from the day when menstruation begins to the day when the next menstruation begins in a woman's body.

[0402] "Changes in physical condition" refers to physical or mental changes experienced by the user.

[0403] "Prediction" refers to estimating future conditions based on past data and current information.

[0404] "Visualization" refers to displaying data or information visually to make it easier to understand.

[0405] "Health and Wellness Advice" means specific suggestions or instructions to improve or maintain a user's health and wellness.

[0406] "Appropriate exercise" refers to an exercise program that is tailored to the user's health condition and goals.

[0407] "Meal Suggestions" refers to meal plans and nutritional guidance tailored to a user's health status and goals.

[0408] A system for implementing the present invention is configured as follows.

[0409] System configuration

[0410] 1. User device: A device that allows users to input information about their menstrual cycle and changes in their physical condition. This includes smartphones and tablets.

[0411] 2. Server: A central processing unit that uses a generative AI model to analyze input data from users and predict hormone fluctuations.

[0412] 3. Generative AI model: This is an artificial intelligence model for predicting hormone fluctuations based on user input data. Specifically, it uses the GPT-3 (registered trademark) model with the Hugging Face transformers library.

[0413] 4. Visualization tools: Software for visually displaying prediction results. Graph drawing libraries such as Matplotlib are examples of such tools.

[0414] Program processing

[0415] 1. Data entry on the user's device: The user enters information such as the start date of the menstrual cycle, the length of the cycle, and changes in physical condition (e.g., headache, fatigue) through a smartphone application.

[0416] 2. Data analysis on the server: The server receives the data sent by the user and generates prompt sentences to input into the generative AI model.

[0417] 3. Prediction by generative AI model: The generative AI model predicts hormone fluctuations based on the prompt text. The prediction results are returned to the server.

[0418] 4. Visualization: The server uses visualization tools to display the prediction results in a visual format such as a graph, allowing users to intuitively understand their own hormone fluctuations.

[0419] 5. Health management advice: Based on the prediction results, the server provides the user with health management advice and suggestions for appropriate exercise and diet.

[0420] Specific examples

[0421] If the user enters "My period will start on January 1, 2023, my cycle will be 28 days long, and I am experiencing headaches and fatigue," the server will generate the following prompt:

[0422] Example prompt sentence:

[0423] User's cycle started on 2023-01-01. Cycle length is 28 days. Symptoms: headache, fatigue. Predict hormone levels for the next cycle.

[0424] By inputting this prompt into a generative AI model, hormone fluctuations are predicted. The prediction results are visualized as graphs using Matplotlib and displayed to the user. Additionally, advice, exercise, and dietary suggestions based on the user's health status are provided.

[0425] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0426] Step 1:

[0427] Data entry on user terminal

[0428] Through a smartphone application, users input information such as the start date of their menstrual cycle, the length of the cycle, and changes in their physical condition (e.g., headache, fatigue). The input data is temporarily stored within the application and then sent to a server.

[0429] Input: start date of menstrual cycle, cycle length, and information on changes in physical condition

[0430] Output: Sending user-entered data

[0431] Step 2:

[0432] Receiving data and generating prompts on the server

[0433] The server receives data sent from the user's device. Based on the received data, it generates prompt sentences to input into the generative AI model. The prompt sentences are created in a format that includes information about the user's menstrual cycle and changes in physical condition.

[0434] Input: User-entered data

[0435] Output: Generated prompt statement

[0436] Step 3:

[0437] Predicting hormone fluctuations using generative AI models

[0438] The server inputs the generated prompt sentences into the generative AI model, which then predicts hormone fluctuations based on the prompt sentences and returns the results to the server.

[0439] Input: prompt statement

[0440] Output: Hormonal fluctuation prediction results

[0441] Step 4:

[0442] Visualization of prediction results

[0443] The server receives the hormone fluctuation prediction results returned by the generative AI model and displays them in a visual format such as a graph using a visualization tool. The prediction results are visualized using a graph drawing library such as Matplotlib.

[0444] Input: Hormonal fluctuation prediction results

[0445] Output: Visualized graph

[0446] Step 5:

[0447] Providing health management advice

[0448] Based on the visualized prediction results, the server provides the user with health management advice and suggestions for appropriate exercise and diet, which are customized according to the user's health condition and goals.

[0449] Input: Visualized prediction results

[0450] Output: Health care advice, exercise and diet suggestions

[0451] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0452] "Example 1"

[0453] One embodiment of the present invention is a system that uses an emotion engine to recognize a user's emotions. This system recognizes emotions from the user's tone of voice, facial expressions, and choice of words, and feeds this information back to a generation AI. The generation AI combines this emotion information with information about the user's menstrual cycle and changes in physical condition to predict hormone fluctuations. Specifically, if the user feels irritated, they feed that information back to the generation AI, which then uses that information to predict hormone fluctuations.

[0454] "Example 2"

[0455] Another embodiment of the present invention is a system in which an emotion engine captures changes in a user's emotions in real time. In this system, the user takes a picture of their face using a smartphone camera, and the emotion engine recognizes emotions from the video. This emotion information is fed back to the generation AI in real time, and the generation AI updates its predictions of hormone fluctuations based on that information.

[0456] "Example 3"

[0457] Furthermore, another embodiment of the present invention is a system in which an emotion engine records changes in a user's emotions and learns the user's individual emotional patterns based on that data. In this system, the user records their daily emotional changes in an app, and the emotion engine learns from that data. Through this learning, the emotion engine understands the user's individual emotional patterns and provides feedback to the generation AI. The generation AI combines these emotional patterns with information such as menstrual cycles and changes in physical condition to gain a deeper understanding of the relationship between hormonal fluctuations and emotions.

[0458] The processing flow of each embodiment will be described below.

[0459] "Example 1"

[0460] Step 1: The user inputs their tone of voice, facial expressions, choice of words, etc. into the emotion engine.

[0461] Step 2: The emotion engine recognizes the user's emotions from this information and feeds that information back to the generation AI.

[0462] Step 3: The generative AI combines this emotional information with information about the user's menstrual cycle, physical changes, etc. to predict hormone fluctuations.

[0463] "Example 2"

[0464] Step 1: The user takes a picture of their face using their smartphone camera.

[0465] Step 2: The emotion engine recognizes emotions from the captured footage and feeds that information back to the generative AI in real time.

[0466] Step 3: The generative AI updates its predictions of hormone fluctuations based on the feedback information.

[0467] "Example 3"

[0468] Step 1: Users record their daily emotional changes in the app.

[0469] Step 2: The emotion engine learns from the recorded data and understands the individual emotional patterns of the user.

[0470] Step 3: The emotion engine feeds back the emotional patterns it understands to the generation AI. Step 4: The generation AI combines the fed-back emotional patterns with information on menstrual cycles, physical changes, etc. to gain a deeper understanding of the relationship between hormonal fluctuations and emotions.

[0471] Example 1

[0472] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0473] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), which have a major impact on women's lives. However, these symptoms are often difficult for those around them to understand, and it is often difficult for women themselves to recognize them as PMS. For this reason, it is necessary for women to accurately understand their own hormone fluctuations and manage their physical condition. Furthermore, emotional fluctuations are also related to hormone fluctuations, so predictions that take this into account are necessary.

[0474] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0475] In this invention, the server includes: a means for a user to input information about their menstrual cycle and changes in physical condition; a means for a terminal to transmit the input information to the server; a means for the server to store the input information in a database; a means for the server to acquire the user's emotional information using an emotion engine; a means for the server to input the input information and the emotional information to a generative AI model; a means for the generative AI model to predict hormone fluctuations; a means for the server to generate the prediction results as graphs or charts; a means for transmitting the graphs or charts generated by the server to the terminal; and a means for the terminal to display the prediction results to the user. This allows the user to visually check their own hormone fluctuations and the accompanying changes in physical condition, enabling them to manage their physical condition. Furthermore, by taking emotional information into account, more accurate predictions of hormone fluctuations are possible.

[0476] "Female hormone fluctuations" refers to the phenomenon in which the concentration of hormones (such as estrogen and progesterone) secreted in a woman's body changes over time.

[0477] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to make predictions and generate information from data.

[0478] The "menstrual cycle" refers to the periodic occurrence of menstrual periods in a woman's body, usually lasting approximately 28 days.

[0479] "Changes in physical condition" refers to changes in an individual's health or physical condition over time.

[0480] The "emotion engine" is a technology that recognizes emotions from the user's tone of voice, facial expressions, choice of words, etc.

[0481] A "database" is a system for efficiently storing, managing, and retrieving data.

[0482] A "generative AI model" is a machine learning model built to realize generative AI, which makes predictions and generates for specific tasks.

[0483] "Graphs and charts" are diagrams that visually represent data and are used to easily understand trends and patterns in the data.

[0484] A "terminal" is a device used by a user to input information and check results, and includes smartphones, personal computers, etc.

[0485] A "server" is a computer system that processes and stores data on a network.

[0486] This invention is a system that visualizes female hormone fluctuations and solves the problems faced by women suffering from premenstrual syndrome (PMS). The system uses a generative AI model to predict hormone fluctuations based on the user's input of information about their menstrual cycle and changes in physical condition, and then visualizes the results.

[0487] The server receives information on menstrual cycles and changes in physical condition provided by the user and stores it in a database. A relational database such as MySQL (registered trademark) or PostgreSQL is used as the database. The server also uses an emotion engine to obtain emotional information from the user's tone of voice, facial expressions, choice of words, etc. A general emotion recognition API is used as the emotion engine.

[0488] The server inputs the input information stored in the database and the emotional information obtained from the emotion engine into the generative AI model. The generative AI model is built using machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch. This generative AI model predicts hormone fluctuations based on the user's input information.

[0489] The prediction results obtained from the generative AI model are generated as graphs and charts by the server using data visualization libraries such as Matplotlib and Plotly, allowing users to visually confirm hormone fluctuations and the resulting changes in their physical condition.

[0490] For example, if a user inputs "My menstrual cycle is 28 days, and I've been feeling tired lately," the device sends this information to the server. The server stores the information in a database and uses an emotion engine to recognize that the user's tone of voice indicates stress. This information is then input into a generative AI model to predict hormone fluctuations. The prediction results are generated as a graph using Matplotlib and sent to the device. Finally, the device displays the graph to the user, allowing them to check their hormone fluctuations and changes in their physical condition.

[0491] Example prompt sentence:

[0492] Please enter information about the user's menstrual cycle and changes in physical condition. For example, "My menstrual cycle is 28 days, and I've been feeling tired lately." Also, please enter emotional information recognized by the emotion engine. For example, "I can hear stress from the tone of your voice."

[0493] This system allows users to accurately understand their own hormone fluctuations and manage their physical condition. Furthermore, by taking emotional information into account, it becomes possible to predict hormone fluctuations with even greater accuracy.

[0494] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0495] Step 1:

[0496] The user inputs information about their menstrual cycle and changes in physical condition.

[0497] Users use devices such as smartphones or PCs to input information about their menstrual cycle and changes in their physical condition. For example, they might input information such as, "My menstrual cycle is 28 days, and I've been feeling tired lately." The input information is saved in an input form on the device.

[0498] Step 2:

[0499] The terminal sends the input information to the server.

[0500] The terminal sends the information entered by the user to the server. At this time, the data is securely transmitted using the HTTPS protocol. The input information is sent to the server in a data format such as JSON.

[0501] Step 3:

[0502] The server stores the input information in a database.

[0503] The server saves the received user input information in a database, using a relational database such as MySQL or PostgreSQL. The input information is stored in the appropriate table in the database.

[0504] Step 4:

[0505] The server acquires the user's emotion information using an emotion engine.

[0506] The server uses an emotion engine to acquire emotional information from the user's tone of voice, facial expressions, and choice of words. A general emotion recognition API is used as the emotion engine. The acquired emotional information is stored in a temporary data store on the server.

[0507] Step 5:

[0508] The server inputs input information and emotion information into the generated AI model.

[0509] The server inputs the input information stored in the database and the emotion information obtained from the emotion engine into the generative AI model. The generative AI model is built using machine learning frameworks such as TensorFlow and PyTorch. The input information and emotion information are passed to the input layer of the generative AI model.

[0510] Step 6:

[0511] Generative AI models predict hormone fluctuations.

[0512] The generative AI model predicts hormone fluctuations based on input information. The prediction algorithm combines past data and machine learning models to predict hormone fluctuations with high accuracy. The prediction results are obtained from the output layer of the generative AI model.

[0513] Step 7:

[0514] The server generates the prediction results as graphs and charts.

[0515] The server uses data visualization libraries such as Matplotlib and Plotly to generate graphs and charts based on the predictions obtained from the generative AI model. The prediction results are visually represented as graphs and charts.

[0516] Step 8:

[0517] The server sends the generated graphs and charts to the device.

[0518] The server then sends the generated graphs and charts to the device, again securely using the HTTPS protocol. The graphs and charts are sent to the device as image files or in HTML format.

[0519] Step 9:

[0520] The terminal displays the prediction results to the user.

[0521] The device then displays the received graphs and charts to the user, allowing the user to visually confirm hormone fluctuations and the resulting changes in physical condition. The displayed graphs and charts are displayed on the device screen.

[0522] (Application example 1)

[0523] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0524] Fluctuations in female hormones are a problem that is difficult for those around them to understand, especially for women who suffer from PMS (premenstrual syndrome), and it is also difficult for them to recognize it as PMS. There is also a need to accurately predict the changes in physical condition and emotions that accompany hormonal fluctuations and take appropriate measures. Another challenge is providing appropriate purchasing advice to users in physical stores.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, and a means for using an emotion engine for recognizing the user's emotions. This makes it possible to solve the problem of women suffering from PMS (premenstrual syndrome), who find it difficult to gain understanding from those around them and to recognize it themselves as PMS. The generation AI can predict hormone fluctuations based on information such as the user's menstrual cycle and physical changes, and visualize the results. Furthermore, the emotion engine can recognize the user's tone of voice and facial expression and provide feedback to the generation AI, thereby improving the accuracy of hormone fluctuation predictions. Additionally, by including a means for providing purchasing advice to users in physical stores, the system can assist users in selecting appropriate products.

[0526] "Female hormone fluctuations" refers to the phenomenon in which the concentrations of hormones such as estrogen and progesterone secreted in a woman's body change over time.

[0527] "Visualization" is the display of data and information in a visual format, such as a graph or chart, to make it easier to understand.

[0528] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[0529] The "emotion engine" is a system that recognizes emotions from the user's tone of voice, facial expressions, choice of words, etc., and analyzes that information.

[0530] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before menstruation.

[0531] The "menstrual cycle" refers to the period of time until menstruation occurs in a woman's body, which is usually about 28 days.

[0532] "Changes in physical condition" refers to changes in the user's physical condition over time, including, for example, headaches and fatigue.

[0533] "Feedback" is the process by which a system receives input from users and uses that information to improve its behavior and predictions.

[0534] "Prediction accuracy" is a measure of how accurately a system can predict future events or conditions.

[0535] A "brick and mortar store" is a store that exists in a physical location and where customers can visit in person to purchase products.

[0536] "Buying advice" means providing advice or suggestions to help users select appropriate products.

[0537] The system for implementing this invention is configured as follows: First, the server includes a means for visualizing female hormone fluctuations, a means for using a generative AI, and a means for using an emotion engine to recognize the user's emotions. This makes it possible to solve the problem of women who suffer from PMS (premenstrual syndrome), who find it difficult to gain understanding from those around them and who find it difficult to recognize that they are suffering from PMS themselves.

[0538] Hardware and software used

[0539] Hardware: Smartphone (microphone, camera)

[0540] Software: Python, scikit-learn (machine learning library), matplotlib (data visualization library), speech_recognition (speech recognition library), emotion_recognition (emotion recognition library)

[0541] Data processing and calculation

[0542] 1. Collecting User-Entered Information:

[0543] Users use their smartphones to input their menstrual cycle and changes in their physical condition, which allows the server to obtain basic health information about the user.

[0544] 2. Emotion recognition:

[0545] The user's voice is recorded using the smartphone's microphone and analyzed using an emotion recognition library, allowing the server to understand the user's emotional state.

[0546] 3. Predicting hormone fluctuations:

[0547] The server uses the collected data to predict hormone fluctuations using a generative AI model (we'll use Linear Regression here), which then predicts the user's hormone level fluctuations and visualizes them in graphs and charts.

[0548] 4. Visualizing the results:

[0549] The server displays the prediction results in graphs and charts and provides them to the user, allowing them to visually understand their own hormone fluctuations.

[0550] 5. Providing Buying Advice:

[0551] In a physical store, the server provides appropriate purchasing advice to users, allowing them to select products that suit their hormone fluctuations.

[0552] Specific examples

[0553] For example, if a user inputs "My menstrual cycle is 28 days, and I've been having a lot of headaches lately," and emotion recognition detects "stress," the server will use generative AI to predict hormone fluctuations and, based on the prediction results, provide advice on purchasing specific supplements during periods of increased stress.

[0554] Prompt Sentence Examples

[0555] The user has a 28-day menstrual cycle and has recently been experiencing a lot of headaches. Emotion recognition detects stress. Using this information, the generative AI predicts hormone fluctuations and recommends purchasing specific supplements during periods of increased stress.

[0556] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0557] Step 1:

[0558] The user uses a smartphone to input their menstrual cycle and changes in their physical condition. Specifically, they input the number of days in their menstrual cycle and recent changes in their physical condition (e.g., headache, fatigue, etc.) into the application's input form. The input data is sent to the server. The input data is text information about the number of days in their menstrual cycle and changes in their physical condition.

[0559] Step 2:

[0560] The server stores the received user input data and then collects voice data for emotion recognition. The user records their own voice using the microphone on their smartphone. The recorded voice data is sent to the server. The input data is an audio file, and the output data is voice data for emotion recognition.

[0561] Step 3:

[0562] The server uses an emotion recognition library (emotion_recognition) to analyze the user's emotions from the received voice data. Specifically, it analyzes the voice data and identifies emotions from the user's tone of voice and choice of words. The input data is an audio file, and the output data is emotional information (e.g., stress, joy, etc.).

[0563] Step 4:

[0564] The server uses a generative AI model (Linear Regression) to predict hormone fluctuations based on the user's menstrual cycle, physical changes, and emotional information. Specifically, this data is input into the model to predict hormone level fluctuations. The input data is the number of days in the menstrual cycle, text information on physical changes, and emotional information, and the output data is the predicted hormone fluctuation results.

[0565] Step 5:

[0566] The server visualizes the prediction results in graphs and charts and provides them to users. Specifically, the matplotlib library is used to visually display the prediction results. The input data is the predicted results of hormone fluctuations, and the output data is graphs and charts.

[0567] Step 6:

[0568] The server provides purchasing advice to users in physical stores. Specifically, it recommends appropriate products (e.g., supplements) to users based on the prediction results. The input data is the predicted hormone fluctuation results, and the output data is purchasing advice.

[0569] Example 2

[0570] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0571] Fluctuations in female hormones can cause symptoms such as premenstrual syndrome (PMS), which can be a significant burden for women. However, these symptoms are often difficult to understand from those around them, and it is often difficult for women themselves to recognize them as PMS. For this reason, it is necessary for women to accurately understand changes in their own physical condition and take appropriate measures. Furthermore, it is necessary to predict hormone fluctuations that take into account individual changes in physical condition and emotional fluctuations, but the current system does not have sufficient accuracy.

[0572] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0573] In this invention, the server includes: a means for a user to input data on changes in physical condition and menstrual cycle; a means for a terminal to transmit the input data to the server; a means for the server to store the data in a database; a means for the server to train a generative AI model using the stored data; a means for a user to take a picture of their face using a camera; a means for the terminal to transmit video data to the server; a means for the server to recognize emotions from the video using an emotion engine; a means for the server to feed back emotional information to the generative AI model; and a means for the server to update the generative AI model and improve the accuracy of hormone fluctuation predictions. This allows women to accurately understand their own changes in physical condition and to gain understanding from those around them, and enables highly accurate predictions of hormone fluctuations that take into account individual changes in physical condition and emotional fluctuations.

[0574] "Female hormone fluctuations" refers to the phenomenon in which the amount and balance of hormones secreted in a woman's body change over time.

[0575] "Generative AI" is a system that uses artificial intelligence technology to generate new information and predictions from data.

[0576] "Changes in physical condition" refers to changes in the user's physical condition, including symptoms such as headaches and fatigue.

[0577] The "menstrual cycle" refers to the periodic cycle in which a woman's menstrual cycle repeats, usually about 28 days.

[0578] A "terminal" is a device that allows a user to input data or use a camera, and includes smartphones and personal computers.

[0579] A "server" is a computer system that stores and processes data, trains generative AI models, and so on.

[0580] A "database" is a system for systematically storing and managing collected data.

[0581] "Training" is the process by which a generative AI model learns from data and improves its prediction accuracy.

[0582] "Camera" means a device for photographing a user's face, including those built into smartphones and personal computers.

[0583] The "emotion engine" is a software system that recognizes user emotions from video data.

[0584] "Feedback" is the process of providing new data or information to a generative AI model to improve its accuracy.

[0585] "Hormonal fluctuation prediction accuracy" is an indicator of how accurately the generative AI model can predict female hormone fluctuations.

[0586] This invention is a system that visualizes female hormone fluctuations and improves the accuracy of hormone fluctuation predictions using generative AI. Specific embodiments of this system are described below.

[0587] First, the user uses a device such as a smartphone or computer to input data about their physical condition and menstrual cycle. For example, they enter information such as "I have a headache" or "the first day of my period" into a dedicated application. This application is designed to make it easy for users to input data.

[0588] The device then sends the entered data to the server in real time, using the HTTPS protocol to ensure data security. The server then stores the received data in a database, typically a relational database such as MySQL or PostgreSQL.

[0589] The server uses the stored data to train the generative AI model. During this training process, the data is preprocessed using Python's Pandas library, features are extracted using Scikit-learn, and the model is trained using TensorFlow. This gives the generative AI model the ability to predict hormone fluctuations based on the user's physical condition and menstrual cycle.

[0590] The user then takes a photo of their face using the smartphone camera, following the instructions of a dedicated application. The device then sends the captured video data to a server in real time. This transmission also uses the HTTPS protocol to ensure data security.

[0591] The server inputs the received video data into an emotion engine to recognize the user's emotions. The emotion engine is developed using facial recognition libraries such as OpenCV and Dlib. The recognized emotion information is fed back to the generative AI model to improve the model's prediction accuracy.

[0592] As a concrete example, consider the case where a user inputs "I have a headache" and takes a photo of their face using their smartphone camera. The data "I have a headache" input by the user is sent from the device to the server and stored in a database. Next, the video data of the user's face is sent to the server, and the emotion engine recognizes the emotion "sad." This emotion information is fed back to the generative AI model, improving the model's prediction accuracy.

[0593] Example prompt sentence:

[0594] "If a user inputs 'I have a headache' as a symptom of their physical condition and takes a picture of their face using their smartphone camera, the emotion engine will recognize the emotion 'sad' from the video. Based on this information, the generative AI model will update its predictions of hormone fluctuations."

[0595] In this way, a system is created in which servers, terminals, and users work together to collect and process data, improving the predictive accuracy of generative AI models.

[0596] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0597] Step 1:

[0598] The user inputs data on changes in physical condition and menstrual cycle.

[0599] Users use a dedicated application on their smartphone or computer to input data about their physical condition and menstrual cycle. For example, they input information such as "I have a headache" or "the first day of my period." The input data is entered into a form in the application, and by pressing the submit button, they proceed to the next step.

[0600] Step 2:

[0601] The terminal sends the input data to the server.

[0602] The device sends the data entered by the user to the server in real time. This transmission uses the HTTPS protocol to ensure data security. The input data is sent to the server in JSON format. For example, it is sent in the format {"Physical condition change": "I have a headache", "Menstruation start date": "2023-10-01"}.

[0603] Step 3:

[0604] The server stores the data in a database.

[0605] The server saves the received data in a database. A relational database such as MySQL or PostgreSQL is used for the database. The server parses the received JSON data and saves it in the appropriate table. For example, data on changes in physical condition is saved in a "Physical Condition Changes" table, and data on menstrual cycle is saved in a "Menstrual Cycle" table.

[0606] Step 4:

[0607] The server uses the stored data to train a generative AI model.

[0608] The server uses the stored data to train a generative AI model. This training process involves preprocessing the data using Python's Pandas library, extracting features using Scikit-learn, and training the model using TensorFlow. Input data is retrieved from the database, preprocessed, and then fed into the model. The output is an updated generative AI model.

[0609] Step 5:

[0610] The user takes a picture of their face using the smartphone camera.

[0611] The user takes a picture of their face using the smartphone camera, following the instructions of a dedicated application. The captured image data is then sent to the server in the next step.

[0612] Step 6:

[0613] The terminal transmits the video data to the server.

[0614] The device sends the captured video data to the server in real time. This transmission also uses the HTTPS protocol to ensure data security. The video data is sent to the server in MP4 format, for example.

[0615] Step 7:

[0616] The server uses an emotion engine to recognize emotions from the video.

[0617] The server inputs the received video data into the emotion engine to recognize the user's emotion. The emotion engine is developed using a face recognition library such as OpenCV or Dlib. The input data is the video data, and the output is the recognized emotion information (e.g., "sad").

[0618] Step 8:

[0619] The server feeds emotional information back to the generative AI model.

[0620] The server feeds the recognized emotional information back to the generative AI model. This feedback allows the generative AI model to predict hormone fluctuations taking into account the user's emotional changes. The input data is emotional information, and the output is an updated generative AI model.

[0621] Step 9:

[0622] The server updates the generated AI model to improve the accuracy of predicting hormone fluctuations.

[0623] The server updates the generative AI model based on the fed-back emotional information. This update improves the accuracy of hormone fluctuation predictions, making it possible to more accurately predict changes in the physical condition of each individual user. The input data is emotional information and physical condition change data, and the output is a highly accurate prediction of hormone fluctuations.

[0624] (Application example 2)

[0625] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0626] Conventional hormone fluctuation prediction systems can predict hormone fluctuations based on the user's physical condition and menstrual cycle, but they cannot reflect the user's emotional state in real time, which means that the prediction accuracy is insufficient. In addition, they cannot suggest appropriate payment methods according to the user's emotional state, which makes it difficult to improve user convenience. There is a need to solve these problems.

[0627] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0628] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for recognizing a user's emotional state in real time, a means for updating a hormone fluctuation prediction model based on the user's emotional state, and a means for proposing an optimal payment method according to the user's emotional state. This makes it possible to predict hormone fluctuations that reflect the user's emotional state in real time, and further to propose an optimal payment method according to the user's emotional state.

[0629] "Means for visualizing female hormone fluctuations" refers to devices or software that visually display female hormone fluctuations based on the user's physical condition data and menstrual cycle data.

[0630] "Means using generative AI" refers to devices or software that use artificial intelligence technology to analyze a user's physical condition data and emotional data, and generate and update predictive models of hormone fluctuations.

[0631] "Means for recognizing a user's emotional state in real time" refers to devices or software that use cameras or sensors to acquire a user's facial expressions and biometric data, and then use emotion recognition algorithms to determine the user's emotions in real time.

[0632] The "means for updating the hormone fluctuation prediction model based on the user's emotional state" refers to a device or software for dynamically correcting and updating the hormone fluctuation prediction model based on the user's emotional data recognized in real time.

[0633] "Means for proposing the optimal payment method according to the user's emotional state" refers to devices or software that analyze the user's emotional state, select the payment method that is most suitable for that state, and propose it to the user.

[0634] A system for implementing the present invention is configured as follows.

[0635] First, the server is equipped with a means for visualizing female hormone fluctuations. This means is a device or software that visually displays female hormone fluctuations based on the user's physical condition data and menstrual cycle data. Specifically, it collects data entered by the user through a smartphone or wearable device and displays it as graphs and charts.

[0636] Next, the server is equipped with a means for using generative AI. This means is a device or software that uses artificial intelligence technology to analyze the user's physical condition data and emotional data and generate and update a prediction model of hormone fluctuations. Specifically, it uses machine learning libraries such as Keras and TensorFlow to analyze the user's data and build a prediction model of hormone fluctuations.

[0637] Furthermore, the server is equipped with a means for recognizing the user's emotional state in real time. This means is a device or software that uses a camera or sensor to capture the user's facial expressions and biometric data, and determines the user's emotions in real time using an emotion recognition algorithm. Specifically, it uses OpenCV to detect faces and an emotion recognition model to determine the user's emotions.

[0638] The server also has a means for updating the hormone fluctuation prediction model based on the user's emotional state. This means is a device or software that dynamically corrects and updates the hormone fluctuation prediction model based on the user's emotional data recognized in real time. This enables more accurate hormone fluctuation prediction that reflects the user's emotional state.

[0639] Finally, the server is equipped with a means for suggesting the optimal payment method according to the user's emotional state. This means is a device or software that analyzes the user's emotional state, selects the payment method that best suits that state, and suggests it to the user. For example, if the user is feeling stressed, it suggests one-touch payment, and if the user is relaxed, it suggests point-back payment.

[0640] For example, a user can take a photo of their face using their smartphone camera, and the emotion engine will recognize their emotions from the video. This emotional information is fed back to the generation AI in real time, and the generation AI will use this information to update its predictions of hormone fluctuations. Furthermore, the system will suggest the most appropriate payment method based on the user's emotional state.

[0641] Examples of prompts include:

[0642] “Recognize the user’s emotional state in real time and update your hormone fluctuation prediction model. If the user is stressed, suggest one-touch payment, and if they are relaxed, suggest point-back payment.”

[0643] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0644] Step 1:

[0645] The user activates the smartphone camera and takes a picture of their face.

[0646] Input: User's face video

[0647] Output: Facial video data

[0648] Specific operation: The user launches the camera app on their smartphone and takes a picture of their face. The camera captures the facial image data.

[0649] Step 2:

[0650] The device acquires facial image data and detects the face using OpenCV.

[0651] Input: Facial image data

[0652] Output: Face region data

[0653] Specific operation: The device inputs face video data into the OpenCV library and runs the face detection algorithm, thereby determining the position and size of the face and generating face region data.

[0654] Step 3:

[0655] The terminal inputs the facial region data into an emotion recognition model to determine the user's emotion.

[0656] Input: Face region data

[0657] Output: Emotion data

[0658] Specific operation: The device inputs the facial region data into the emotion recognition model built with Keras and executes the emotion recognition algorithm, thereby determining the user's emotion (e.g., stress, relaxation, etc.) and generating emotion data.

[0659] Step 4:

[0660] The device sends emotional data to a server, and the generative AI model is used to update the hormone fluctuation prediction model.

[0661] Input: Emotion data

[0662] Output: Updated hormone fluctuation prediction model

[0663] Specific operation: The device sends emotional data to the server, which then uses the generated AI model to update the hormone fluctuation prediction model, thereby generating an updated hormone fluctuation prediction model that reflects the user's emotional state.

[0664] Step 5:

[0665] Based on the updated hormone fluctuation prediction model, the server proposes the optimal payment method according to the user's emotional state.

[0666] Input: Updated hormone fluctuation prediction model

[0667] Output: Best payment method suggestions

[0668] Specific operation: The server analyzes the updated hormone fluctuation prediction model and selects the payment method (e.g., one-touch payment, point-back payment, etc.) that best suits the user's emotional state. The selected payment method is then proposed to the user.

[0669] Step 6:

[0670] The user reviews and selects the proposed payment method.

[0671] Input: Best payment method suggestions

[0672] Output: Selected payment method

[0673] Specific operation: The user reviews the payment method suggestions displayed on the smartphone screen and selects the payment method that best suits them. The selected payment method is recorded in the system.

[0674] Example 3

[0675] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0676] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), significantly affecting the quality of women's lives. However, it is difficult to accurately grasp and predict these hormone fluctuations and the accompanying changes in physical condition and emotions. In addition, women who suffer from PMS often find it difficult to gain understanding from those around them, and even to recognize it as PMS themselves. To solve these issues, a system is needed that visualizes the relationship between hormone fluctuations and emotions and improves prediction accuracy.

[0677] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for collecting information on menstrual cycles and changes in physical condition input by the user, a means for the generation AI to predict hormone fluctuations based on this information, a means for visualizing the prediction results and displaying them to the user, a means for the user to input daily emotional changes, a means for an emotion engine to learn the user's emotional data and understand emotional patterns, a means for feeding back the emotional patterns to the generation AI, and a means for the generation AI to reevaluate the relationship between hormone fluctuations and emotions and improve prediction accuracy. This allows the user to accurately understand the relationship between their own hormone fluctuations and emotions and predict and manage symptoms such as PMS.

[0678] "Means for visualizing female hormone fluctuations" refers to technology for displaying fluctuations in a user's hormone levels in a visual format such as graphs or charts.

[0679] "Means using generative AI" refers to methods that use artificial intelligence technology to analyze data and make predictions and judgments.

[0680] "Means for collecting information on menstrual cycles and changes in physical condition entered by users" refers to technology for collecting data on menstrual cycles and changes in physical condition entered by users via smartphones or other devices.

[0681] "The means by which generative AI predicts hormone fluctuations based on this information" refers to artificial intelligence technology that analyzes collected data on menstrual cycles and changes in physical condition to predict fluctuations in hormone levels.

[0682] "Means for visualizing and displaying predicted results to users" refers to technology for displaying the results of hormone fluctuations predicted by the generative AI to users in a visual format such as graphs or charts.

[0683] "Means for users to input their daily emotional changes" refers to an interface that allows users to input their daily emotional changes through a smartphone or other device.

[0684] "Means for the emotion engine to learn the user's emotional data and understand emotional patterns" refers to technology that analyzes the emotional data entered by the user and learns the patterns of change in the user's emotions.

[0685] "Means for feeding back emotional patterns to the generative AI" refers to a method in which the emotion engine provides the learned emotional patterns to the generative AI, allowing the generative AI to improve its prediction accuracy based on that information.

[0686] "Means for generative AI to reevaluate the relationship between hormone fluctuations and emotions and improve prediction accuracy" refers to a technology in which generative AI combines and analyzes data on emotional patterns and hormone fluctuations to improve the accuracy of hormone fluctuation predictions.

[0687] This invention is a system that visualizes female hormone fluctuations and predicts and manages the relationship between hormone fluctuations and emotions using generative AI. Specific embodiments of this system are described below.

[0688] First, the user uses a smartphone application to input information about their menstrual cycle and changes in their physical condition. This application provides a user interface and is designed to allow users to easily input data. The input data is then sent from the device (smartphone) to the server. The communication is encrypted and uses a secure protocol (e.g., HTTPS).

[0689] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-4). The generative AI model analyzes data on the user's menstrual cycle and physical changes to predict hormone fluctuations. The prediction results are sent from the server to the device, which visualizes them as graphs and charts and displays them to the user. The user can view this information on the app's dashboard.

[0690] Additionally, users input their daily emotional changes into the app. This emotional data is also sent from the device to the server. The server then uses an emotion engine to analyze the emotional data and learn the user's emotional patterns. The emotion engine understands the user's emotional changes and feeds those patterns back into the generative AI model.

[0691] The generative AI model combines emotional patterns with data on menstrual cycles and physical changes to reassess the relationship between hormone fluctuations and emotions. This improves the accuracy of hormone predictions. Ultimately, users will be able to accurately understand the relationship between their own hormone fluctuations and emotions, and predict and manage symptoms such as PMS.

[0692] Examples:

[0693] The user enters "Menstruation start date: October 1, 2023," "Physical condition: Easily tired," and "Emotions: Irritable" into a smartphone app. The device sends this data to the server. The server passes the data to the generative AI model, which predicts hormone fluctuations. The generative AI model predicts "next expected menstruation date: October 29, 2023," and "Hormonal fluctuations: High stress period," and returns the results to the server. The server sends the prediction results to the device, which displays them as graphs. The user can check this information on the app's dashboard.

[0694] Example prompt sentence:

[0695] "Please predict the next menstrual period and hormone fluctuations based on the menstrual cycle and physical condition change data entered by the user. Also, please take into account the emotional data and predict emotional changes."

[0696] In this way, the server, terminal, and user each play their respective roles, and the system utilizes the generation AI and emotion engine to visualize the relationship between hormone fluctuations and emotions. The flow of the identification process in Example 3 will be described with reference to Fig. 21.

[0697] Step 1:

[0698] The user inputs information about their menstrual cycle and changes in physical condition.

[0699] Users open the smartphone app and enter the start date of their period and any changes in their physical condition (e.g., fatigue, headaches, etc.). The entered data is saved on the user's smartphone.

[0700] Step 2:

[0701] The terminal sends the input data to the server.

[0702] The device sends the data entered by the user to the server in real time. The data is encrypted and uses a secure communication protocol (e.g., HTTPS). The input data includes information about the start date of menstruation and changes in physical condition. The server receives this data and stores it in a database.

[0703] Step 3:

[0704] The server passes the data to a generative AI model to predict hormone fluctuations.

[0705] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-4). The generative AI model analyzes data on the user's menstrual cycle and changes in physical condition to predict hormone fluctuations. The input data is information about the start date of menstruation and changes in physical condition, and the output data is the expected date of the next menstrual period and predicted fluctuations in hormone levels.

[0706] Step 4:

[0707] The server sends the prediction results to the device.

[0708] The server sends predictions from the generative AI model, including the expected date of the next menstrual period and hormone level fluctuations, to the device, which receives this data and prepares it for display to the user.

[0709] Step 5:

[0710] The device visualizes the prediction results and displays them to the user.

[0711] The device visualizes the received prediction results as graphs and charts and displays them to the user. The user can view this information on the app's dashboard. The input data is the prediction results, and the output data is the visually displayed graphs and charts.

[0712] Step 6:

[0713] Users input their daily emotional changes.

[0714] Users input their daily emotional changes (e.g., irritation, sadness, etc.) through the app. The input data is information about the user's emotions. This data is stored on the user's smartphone.

[0715] Step 7:

[0716] The device transmits the emotion data to the server.

[0717] The device sends the emotion data entered by the user to the server. The data is encrypted and uses a secure communication protocol. The input data is information about the user's emotions, and the server receives this data and stores it in a database.

[0718] Step 8:

[0719] The server passes the data to the emotion engine, which learns the emotion patterns.

[0720] The server passes the received emotion data to the emotion engine, which analyzes the data and learns the user's emotion patterns. The input data is information about the user's emotions, and the output data is the learned emotion patterns.

[0721] Step 9:

[0722] The server feeds back the emotional patterns to the generative AI model.

[0723] The server feeds back the emotional patterns obtained from the emotion engine to the generative AI model. The generative AI model reevaluates the relationship between hormone fluctuations and emotions based on this feedback. The input data are the learned emotional patterns, and the output data are the reevaluated predictions of hormone fluctuations.

[0724] Step 10:

[0725] The generative AI model reassessed the relationship between hormone fluctuations and emotions to improve prediction accuracy.

[0726] The generative AI model combines and analyzes emotional patterns with data on menstrual cycles and changes in physical condition, and reevaluates the relationship between hormone fluctuations and emotions. This improves the accuracy of hormone fluctuation predictions. The input data are emotional patterns and data on menstrual cycles and changes in physical condition, and the output data is improved prediction accuracy.

[0727] (Application example 3)

[0728] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0729] Fluctuations in female hormones and the accompanying changes in physical condition and emotions are extremely important information for each woman, but accurately understanding this information and taking appropriate measures can be difficult. Women who suffer from PMS (premenstrual syndrome) often struggle to gain understanding from those around them, and even to recognize it as PMS themselves. Furthermore, it is not easy to select the most appropriate health products and supplements based on this information. A system to solve these problems is needed.

[0730] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0731] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for inputting a user's menstrual cycle, physical condition changes, and emotional data, a means for the generation AI to analyze the input data and predict hormone fluctuations, a means for an emotion engine to learn the user's emotional patterns, and a means for suggesting optimal health products and supplements to the user based on the prediction results. This enables the user to accurately understand their own hormone fluctuations and emotional patterns and select optimal health products and supplements.

[0732] "Female hormone fluctuations" refers to the fluctuations in hormones such as estrogen and progesterone that change periodically in a woman's body.

[0733] "Visualization" refers to displaying data or information visually to make it easier to understand.

[0734] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and make predictions and generate results.

[0735] The "menstrual cycle" refers to the period from the first day of a woman's menstrual period to the first day of her next menstrual period.

[0736] "Changes in physical condition" refers to changes in an individual's health or physical condition.

[0737] "Emotional data" refers to data that records a user's emotional state and fluctuations.

[0738] An "emotion engine" refers to a system that analyzes users' emotional data and learns their emotional patterns.

[0739] "Health Products" refers to products used to maintain or improve health.

[0740] "Supplements" refers to products such as vitamins and minerals that are taken as dietary supplements.

[0741] "PMS (premenstrual syndrome)" refers to the general term for the physical and psychological symptoms that women experience before their period.

[0742] This invention is a system that visualizes female hormone fluctuations and uses generative AI to analyze a user's menstrual cycle, physical condition changes, and emotional data. A specific embodiment of this system is described below.

[0743] System configuration

[0744] The system consists of the following main components:

[0745] 1. User device: A mobile device such as a smartphone or tablet.

[0746] 2. Server: A cloud server for data analysis and running generative AI models.

[0747] 3. Database: A database for storing the user's menstrual cycle, physical condition changes, and emotional data.

[0748] Hardware and software used

[0749] Hardware: Smartphone, cloud server

[0750] Software: Python, TensorFlow, Scikit-learn

[0751] Data Entry

[0752] The user enters the following data via a smartphone application:

[0753] Menstrual cycle data

[0754] Data on changes in physical condition

[0755] Emotional Data

[0756] Data analysis

[0757] The server receives the data sent by the user and performs the following operations:

[0758] 1. Analysis using a generative AI model: Using a generative AI model, we analyze menstrual cycle and physical condition change data to predict hormone fluctuations.

[0759] 2. Learning by emotion engine: The emotion engine analyzes the user's emotion data and learns emotional patterns.

[0760] Visualizing the results

[0761] The analysis results are visualized on a smartphone screen, allowing users to check their own hormone fluctuations and emotional patterns at any time.

[0762] Product suggestion

[0763] Based on the analysis, the server will suggest optimal health products and supplements to the user, based on the user's hormonal fluctuations and emotional patterns.

[0764] Specific examples

[0765] For example, if a user enters the following data:

[0766] Menstrual cycle data: 28 days, 30 days, 27 days, 29 days

[0767] Physical condition change data: 5, 6, 4, 5

[0768] Emotion data: 3, 4, 2, 3

[0769] Based on this data, the generative AI predicts hormone fluctuations, and the emotion engine learns emotional patterns, ultimately suggesting "vitamin C supplements" to the user.

[0770] Prompt Sentence Examples

[0771] Enter the user's menstrual cycle data, physical condition data, and emotional data. Generative AI predicts hormone fluctuations based on this data, and the emotional engine learns emotional patterns. Based on the prediction results, it recommends optimal health products and supplements.

[0772] This way, users can easily find the products that best suit their health condition.

[0773] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0774] Step 1:

[0775] The user starts the smartphone application and inputs menstrual cycle data, physical condition change data, and emotion data.

[0776] Input: menstrual cycle data, physical condition change data, emotional data

[0777] Output: Sending input data

[0778] Specific behavior: When a user enters data into the app's input form and presses the "Submit" button, the data is sent to the server.

[0779] Step 2:

[0780] The server receives the data sent by the user and stores it in a database.

[0781] Input: Menstrual cycle data, physical condition change data, and emotional data sent by the user

[0782] Output: Data stored in the database

[0783] Specific operation: The server analyzes the received data, converts it into an appropriate format, and saves it in the database.

[0784] Step 3:

[0785] The server uses the generated AI model to analyze menstrual cycle data and physical condition change data and predict hormone fluctuations.

[0786] Input: Menstrual cycle data and physical condition change data stored in the database

[0787] Output: Predicted hormone fluctuations

[0788] How it works: The server runs the generative AI model and predicts hormone fluctuations based on the input data. The prediction results are temporarily stored in memory.

[0789] Step 4:

[0790] The server uses an emotion engine to analyze the emotion data and learn the user's emotion patterns.

[0791] Input: Emotion data stored in a database

[0792] Output: Learning result of emotion patterns

[0793] Specific operation: The server runs the emotion engine and learns the user's emotion patterns based on the emotion data. The learning results are temporarily stored in memory.

[0794] Step 5:

[0795] The server integrates the results of hormone fluctuation predictions and learning of emotional patterns to suggest optimal health products and supplements to users.

[0796] Input: Predicted hormone fluctuations, learning results of emotional patterns

[0797] Output: Health product and supplement suggestions

[0798] Specific operation: Based on the integrated data, the server selects the health products and supplements that are best suited to the user and generates recommendations.

[0799] Step 6:

[0800] The server sends the proposal to the user's smartphone, and the user confirms the proposal.

[0801] Input: Proposal content for health products and supplements

[0802] Output: Suggestions displayed on the user's smartphone

[0803] Specific operation: The server sends the proposal to the user's smartphone, and the app displays the proposal. The user can check the proposal and purchase the product if necessary.

[0804] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0805] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0806] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0807] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0808] [Second embodiment]

[0809] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0810] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0811] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0812] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0813] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0814] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0815] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0816] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0817] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0818] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0819] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0820] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0821] "Example 1"

[0822] One embodiment of the present invention is a female hormone fluctuation visualization system that uses generative AI. In this system, generative AI predicts hormone fluctuations based on information input by the user, such as information on menstrual cycles and changes in physical condition. The predicted results are presented to the user in the form of graphs or charts, visualizing hormone fluctuations and the accompanying changes in physical condition.

[0823] "Example 2"

[0824] Furthermore, the Generative AI learns from user feedback. Specifically, users input actual data on changes in their physical condition and menstrual cycle into the system, and the Generative AI updates the hormone fluctuation prediction model based on that data. This improves the accuracy of hormone fluctuation predictions, making it possible to more accurately predict changes in each user's physical condition.

[0825] "Example 3"

[0826] In another embodiment of the present invention, a female hormone fluctuation visualization system using generative AI is implemented as a smartphone application. The user inputs information about their menstrual cycle and changes in their physical condition via their smartphone, and the generative AI predicts hormone fluctuations based on this information. The predicted results are visualized on the smartphone screen, allowing the user to check their own hormone fluctuations at any time.

[0827] The processing flow of each embodiment will be described below.

[0828] "Example 1"

[0829] Step 1: The user enters information about their menstrual cycle and physical condition into the system.

[0830] Step 2: Generative AI predicts hormone fluctuations based on input information.

[0831] Step 3: Present the prediction results to the user in the form of graphs, charts, etc., to visualize hormone fluctuations and the resulting changes in physical condition.

[0832] "Example 2"

[0833] Step 1: The user enters actual data about their physical condition and menstrual cycle into the system.

[0834] Step 2: The generative AI updates the hormone fluctuation prediction model based on the input data.

[0835] Step 3: Using the updated prediction model, the accuracy of predicting hormone fluctuations is improved, allowing for more accurate prediction of changes in each user's physical condition.

[0836] "Example 3"

[0837] Step 1: The user inputs information about their menstrual cycle and changes in physical condition via their smartphone. Step 2: The generative AI predicts hormone fluctuations based on this information.

[0838] Step 3: The prediction results are visualized on the smartphone screen, allowing users to check their own hormone fluctuations at any time.

[0839] Example 1

[0840] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0841] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), significantly impacting the quality of women's lives. However, there is a lack of means to accurately predict and visualize these hormone fluctuations and the accompanying changes in physical condition, making it difficult for women themselves to understand their own changes in physical condition, and for those around them to understand them. Furthermore, existing systems face the challenge of making it difficult to effectively utilize user feedback to improve prediction accuracy.

[0842] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0843] In this invention, the server includes: a means for the user to provide input information through a terminal; a means for the server to receive the input information and organize the data; a means for the server to send prompts to the generative AI model; a means for the generative AI model to predict hormone fluctuations; a means for the server to receive the prediction results and organize the data; a means for the server to visualize the prediction results as graphs or charts; a means for the server to send the visualization results to the user's terminal; and a means for the user to check the visualization results on the terminal. This allows the user to visually understand their own hormone fluctuations and the accompanying changes in their physical condition, making it easier for others to understand. Furthermore, the generative AI model can improve the accuracy of hormone fluctuation predictions by learning from user feedback.

[0844] "User" refers to an individual who uses the system to provide information about their menstrual cycle and changes in physical condition.

[0845] "Device" refers to the electronic device used by the user to input information and check the results. Specifically, this includes smartphones and personal computers.

[0846] "Server" refers to the computer system responsible for receiving input information from users, processing the data, and sending it to the generative AI model.

[0847] "Generative AI model" refers to an artificial intelligence model that predicts hormone fluctuations based on data provided by the user.

[0848] "Prompt sentence" refers to an instruction sent to a generative AI model to make a prediction.

[0849] "Hormonal fluctuations" refers to the fluctuations in hormone levels, such as estrogen and progesterone, that occur during a woman's menstrual cycle.

[0850] "Organizing data" refers to converting user-provided information into an appropriate format that makes it easier to use for analysis and prediction.

[0851] "Visualization" refers to displaying the predicted hormone fluctuation results in a visual format such as a graph or chart.

[0852] "Feedback" refers to additional or corrective information provided by users that is used to improve the predictive accuracy of generative AI models.

[0853] This invention is a system for visualizing female hormone fluctuations using a generative AI model. Specific embodiments of this system are described below.

[0854] First, the user provides information about their menstrual cycle and changes in their physical condition through a device. The device can be a common electronic device such as a smartphone or PC. The user enters the following data through a dedicated application or web form:

[0855] Menstrual cycle data: [28, 30, 27, 29]

[0856] Physical condition change data: ["headache", "fatigue", "depression"]

[0857] The server then receives the data sent by the user and processes it using the Python Pandas library to organize the data into a data frame, which converts the data into a format that is easy to use for analysis and prediction.

[0858] The server then sends the organized data to a generative AI model, such as OpenAI's GPT-4, which is an advanced artificial intelligence model. The server then generates a prompt like the following and sends it to the generative AI model:

[0859] User's menstrual cycle data: [28, 30, 27, 29]

[0860] User's physical condition data: ["headache", "fatigue", "depression"]

[0861] Use this data to predict hormone fluctuations during your next menstrual cycle.

[0862] The generative AI model predicts hormone fluctuations based on the prompt text sent, generating predictions such as the following:

[0863] The hormonal fluctuations during your next menstrual cycle are as follows:

[0864] Day 1: Estrogen levels drop

[0865] Day 5: Progesterone levels rise

[0866] Day 14: Estrogen levels peak

[0867] Day 21: Progesterone levels peak

[0868] The server receives the prediction results returned by the generative AI model and organizes them as a data frame using Pandas. The organized prediction results are then visualized as graphs and charts using the Matplotlib library. For example, a line chart showing the fluctuations in estrogen and progesterone is generated.

[0869] Finally, the server sends the generated graphs and charts to the user's device, where the user can view them and visually understand the hormonal fluctuations and accompanying physical changes during the next menstrual cycle.

[0870] This system allows users to accurately understand their own hormone fluctuations and manage their physical condition. In addition, the generative AI model can improve the accuracy of hormone fluctuation predictions by learning from user feedback.

[0871] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0872] Step 1:

[0873] The user provides input information through the terminal.

[0874] Specifically, users use a smartphone or computer to enter information about their menstrual cycle and changes in their physical condition into a dedicated application or web form.

[0875] An example of the data to be entered is as follows:

[0876] Menstrual cycle data: [28, 30, 27, 29]

[0877] Physical condition change data: ["headache", "fatigue", "depression"]

[0878] Input: Menstrual cycle data and physical condition change data entered by the user into the device

[0879] Output: Input data sent from the device to the server

[0880] Step 2:

[0881] The server receives the input and organizes the data.

[0882] The server receives the data submitted by the user and organizes it into a data frame using Python's Pandas library.

[0883] Specifically, the data is organized as follows:

[0884] Convert menstrual cycle data and physical condition change data into a data frame

[0885] Input: Menstrual cycle data and physical condition change data sent from the device

[0886] Output: A neat data frame

[0887] Step 3:

[0888] The server sends a prompt to the generative AI model.

[0889] The server generates prompt sentences based on the organized data and sends them to the generative AI model.

[0890] An example prompt is:

[0891] User's menstrual cycle data: [28, 30, 27, 29]

[0892] User's physical condition data: ["headache", "fatigue", "depression"]

[0893] Use this data to predict hormone fluctuations during your next menstrual cycle.

[0894] Input: A cleaned data frame

[0895] Output: The prompt sent to the generative AI model

[0896] Step 4:

[0897] Generative AI models predict hormone fluctuations.

[0898] A generative AI model (e.g., GPT-4) predicts hormone fluctuations based on the prompt text sent.

[0899] Specifically, it generates the following predictions:

[0900] The hormonal fluctuations during your next menstrual cycle are as follows:

[0901] Day 1: Estrogen levels drop

[0902] Day 5: Progesterone levels rise

[0903] Day 14: Estrogen levels peak

[0904] Day 21: Progesterone levels peak

[0905] Input: The prompt sent to the generative AI model

[0906] Output: The predicted results returned by the generative AI model

[0907] Step 5:

[0908] The server receives the prediction results and organizes the data.

[0909] The server receives the prediction results returned by the generative AI model and organizes them as a data frame again using Pandas.

[0910] Specifically, the data is organized as follows:

[0911] Convert the prediction results into a data frame

[0912] Input: Prediction results returned by the generative AI model

[0913] Output: A data frame of organized prediction results

[0914] Step 6:

[0915] The server visualizes the prediction results as graphs and charts.

[0916] The server visualizes the organized prediction results as graphs and charts using the Matplotlib library.

[0917] Specifically, the graph is generated as follows:

[0918] Create a line chart showing fluctuations in estrogen and progesterone

[0919] Input: Data frame of organized prediction results

[0920] Output: Generated graphs and charts

[0921] Step 7:

[0922] The server sends the visualization results to the user's terminal.

[0923] The server sends the generated graphs and charts to the user's terminal.

[0924] Specifically, the data may be sent using an HTTP response or through a dedicated application.

[0925] Input: Generated graphs and charts

[0926] Output: Visualization results sent to the user's device

[0927] Step 8:

[0928] The user checks the visualization results on the device.

[0929] Users can check the graphs and charts received on their device to visually understand the hormonal fluctuations and accompanying changes in physical condition during their next menstrual cycle.

[0930] Specifically, it displays graphs and charts through an application or web browser.

[0931] Input: Visualization results sent from the server

[0932] Output: Graphs and charts that users can view on their devices

[0933] (Application example 1)

[0934] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0935] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS) and have a significant impact on women's quality of life. However, these hormone fluctuations and the accompanying changes in physical condition are difficult for those around them to understand, and it is also difficult for women to recognize them themselves. Furthermore, there are challenges in receiving appropriate health management and product recommendations.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0937] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for predicting hormone fluctuations based on information such as the user's menstrual cycle and changes in physical condition and visualizing the results, and a means for providing health care advice and product recommendations suited to the user. As a result, by accurately predicting and visualizing female hormone fluctuations, the user can more easily understand changes in their own physical condition and receive appropriate health care and product recommendations.

[0938] "Female hormone fluctuations" are changes in the levels of hormones such as estrogen and progesterone that fluctuate periodically in a woman's body.

[0939] "Visualization" refers to the display of data or information in a visual format such as a graph or chart.

[0940] "Generative AI" is an artificial intelligence technology that makes predictions and generates information based on input from users.

[0941] The "menstrual cycle" refers to the period from the first day of a woman's menstrual period to the first day of her next menstrual period.

[0942] "Changes in physical condition" refers to any physical or mental changes or symptoms experienced by the user.

[0943] "Health Management Advice" means specific suggestions or instructions to improve or maintain a user's health.

[0944] "Product suggestion" refers to recommending appropriate products based on the user's needs and condition.

[0945] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before their period.

[0946] "Feedback" refers to opinions and reactions provided by users, and is information used to improve the performance of a system.

[0947] The system for implementing this invention visualizes female hormone fluctuations and uses a generation AI to provide health management advice and product recommendations tailored to the user. Specific embodiments of this system are described below.

[0948] System configuration

[0949] The system consists of the following main components:

[0950] 1. User device: The user enters information about their menstrual cycle and changes in physical condition on a device such as a smartphone or tablet.

[0951] 2. Server: Runs the generative AI model, predicts hormone fluctuations based on user input, and visualizes the results.

[0952] 3. Generative AI model: Predicts hormone fluctuations based on user input and generates health management advice and product suggestions.

[0953] Program processing

[0954] 1. On the user's device, enter:

[0955] Users use a smartphone or tablet to enter information such as the start date of their menstrual cycle, the length of the cycle, and changes in their physical condition (e.g., fatigue, abdominal pain).

[0956] 2. Data processing on the server:

[0957] The server receives the data sent by the user and inputs it into a generative AI model, which predicts hormone fluctuations based on the user's menstrual cycle and changes in physical condition.

[0958] 3. Prediction and visualization of hormone fluctuations:

[0959] The generative AI model visualizes the predicted hormone fluctuations as graphs and charts, allowing users to visually understand their own hormone fluctuations.

[0960] 4. Health care advice and product suggestions:

[0961] The generative AI model uses predicted hormone fluctuations and information on changes in the user's physical condition to generate appropriate health management advice and product recommendations, such as suggesting supplements or fitness programs needed at specific times.

[0962] Hardware and software used

[0963] Hardware: smartphones, tablets, servers

[0964] Software: Generative AI models, data processing libraries (e.g., Python, Matplotlib)

[0965] Specific examples

[0966] The user uses their smartphone to enter the following information:

[0967] Menstrual cycle start date: October 1, 2023

[0968] Cycle length: 28 days

[0969] Changes in physical condition: fatigue, abdominal pain

[0970] Using this information, the generative AI model predicts hormone fluctuations over the next 28 days and generates prompts like this:

[0971] The user's menstrual cycle starts on October 1, 2023, and is 28 days long. The user is experiencing fatigue and abdominal pain. Based on this information, predict hormone fluctuations for the next 28 days and provide appropriate health management advice.

[0972] The generative AI model predicts hormone fluctuations based on the prompt text and visualizes them as graphs. It also provides users with appropriate health management advice and product recommendations, such as suggesting supplements or fitness programs needed at specific times.

[0973] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0974] Step 1:

[0975] The user uses a smartphone or tablet to input information such as the start date of the menstrual cycle, the length of the cycle, and changes in physical condition (e.g., fatigue, abdominal pain).

[0976] Input: menstrual cycle start date, cycle length, and information on changes in physical condition

[0977] Output: User input data

[0978] Specific operation: The user enters the required information into the application's input form and presses the submit button.

[0979] Step 2:

[0980] The terminal transmits the user's input data to the server.

[0981] Input: User-entered data

[0982] Output: User data sent to the server

[0983] Specific operation: The terminal sends the input data to the server as an HTTP request.

[0984] Step 3:

[0985] The server receives the data sent by the user and converts it into a data format for input into the generative AI model.

[0986] Input: User data sent to the server

[0987] Output: Data format that can be input to a generative AI model

[0988] Specific operation: The server parses the received data and converts it into the required format (e.g., JSON format).

[0989] Step 4:

[0990] The server uses the generated AI model to predict hormone fluctuations based on information about the user's menstrual cycle and changes in physical condition.

[0991] Input: Data format that can be input to the generative AI model

[0992] Output: Predicted hormone fluctuation data

[0993] Specific operation: The server inputs data into the generative AI model and obtains predicted results for hormone fluctuations.

[0994] Step 5:

[0995] The server visualizes the predicted hormone fluctuation data as graphs and charts.

[0996] Input: Predicted hormone fluctuation data

[0997] Output: Visualized graphs and charts

[0998] What it does: The server uses libraries such as Matplotlib to convert hormone fluctuation data into graphs and charts.

[0999] Step 6:

[1000] The server uses the generated AI model to generate health management advice and product suggestions appropriate for the user.

[1001] Input: Predicted hormone fluctuation data, user's physical condition change information

[1002] Output: Health care advice and product suggestions

[1003] Specific operation: The server inputs data into the generative AI model to generate health management advice and product suggestions.

[1004] Step 7:

[1005] The server transmits visualized graphs and charts, as well as health management advice and product suggestions, to the user's terminal.

[1006] Input: visualized graphs and charts, health management advice and product suggestions

[1007] Output: Data sent to the user's terminal

[1008] Specific operation: The server sends the generated data to the user terminal as an HTTP response.

[1009] Step 8:

[1010] The user terminal receives the data sent from the server and displays it on the application.

[1011] Input: Data sent from the server

[1012] Output: Graphs and charts displayed on the application, health management advice and product suggestions

[1013] Specific operation: The user device analyzes the received data and displays it on the application's UI.

[1014] Example 2

[1015] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1016] Fluctuations in female hormones cause symptoms such as PMS (premenstrual syndrome), placing a significant burden on women. However, these symptoms are often difficult to understand by those around them, and women themselves often find it difficult to recognize them as PMS. Conventional systems have low accuracy in predicting hormone fluctuations based on individual users' physical changes and menstrual cycles, making it difficult to take appropriate measures. Therefore, there is a need for a system that can accurately predict hormone fluctuations based on a user's physical changes and menstrual cycles, and visualize the results.

[1017] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1018] In this invention, the server includes means for the user to input data regarding changes in physical condition and menstrual cycle, means for transmitting the data to the server, means for storing the data, means for providing the data to a generative AI model, means for the generative AI model to predict hormone fluctuations based on the data and visualize the results, and means for the generative AI model to learn from feedback from the user and improve the accuracy of hormone fluctuation predictions.This makes it possible to solve the problem that women who suffer from PMS have difficulty getting understanding from those around them and also have difficulty recognizing that they are suffering from PMS themselves.

[1019] "User" refers to an individual who uses the system to input data regarding changes in physical condition and menstrual cycle.

[1020] "Changes in physical condition" refers to fluctuations in the user's physical or mental state, and specifically includes the start date of menstruation, body temperature, mood changes, etc.

[1021] "Menstrual cycle" refers to a woman's menstrual cycle and includes the period from the first day of menstruation to the first day of the next menstruation.

[1022] "Data" refers to information entered by the user regarding changes in physical condition and menstrual cycle.

[1023] "Terminal" refers to a device used by a user to input data, and specifically includes smartphones and personal computers.

[1024] "Server" refers to the computer system that receives, stores, and provides user-submitted data to the generative AI model.

[1025] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to learn from user data and predict hormone fluctuations.

[1026] "Hormonal fluctuations" refers to fluctuations in hormone levels that occur in a woman's body, particularly those associated with the menstrual cycle.

[1027] "Prediction" refers to the generative AI model estimating future hormone fluctuations based on user data.

[1028] "Visualization" refers to displaying the results of hormone fluctuations predicted by the generative AI model in a way that is easy for users to understand.

[1029] "Feedback" refers to additional data or opinions that users provide to the system, which are used to train the generative AI model.

[1030] "PMS (premenstrual syndrome)" refers to the physical and mental symptoms women experience before their period, including abdominal pain, headaches, and mood swings.

[1031] This invention is a system in which a user inputs data on changes in physical condition and menstrual cycle, and a generative AI model predicts hormone fluctuations based on that data and visualizes the prediction results. Specific embodiments of this system are described below.

[1032] Hardware and Software Configuration

[1033] Terminal

[1034] Users enter data using devices such as smartphones or computers. A dedicated application or website is installed on the device, and users enter data on changes in their physical condition and menstrual cycle through this.

[1035] server

[1036] The server receives and stores data sent by users. The server has a database and organizes and stores data for each user. The server also provides data to the generative AI model.

[1037] Generative AI Models

[1038] The generative AI model predicts hormone fluctuations based on data provided by the server, and uses machine learning algorithms to learn from user feedback and improve prediction accuracy.

[1039] Data processing and calculation

[1040] Entering and Submitting Data

[1041] The user enters data about their physical condition and menstrual cycle through the device. For example, the user might enter "My period started on October 1, 2023." The device then encrypts this data and sends it to the server using the HTTPS protocol.

[1042] Data storage

[1043] The server stores the received data in a database. For example, it stores it in the format "User ID: 12345, Menstruation Start Date: October 1, 2023".

[1044] Data provision

[1045] The server retrieves the data for "User ID: 12345" from the database and provides it to the generative AI model.

[1046] Model Updates and Predictions

[1047] The generative AI model updates the neural network based on the provided data and adjusts the parameters for predicting the start date of the next period. For example, the generative AI model learns from "User ID: 12345's menstrual start data on October 1, 2023" and more accurately predicts the start date of the next period.

[1048] Providing prediction results

[1049] The generative AI model generates a prediction result and returns it to the server in the format "User ID: 12345, Next Menstrual Start Date: October 29, 2023." The server then sends this result to the device, where the user can check it in the "Prediction Results" section of the app.

[1050] Specific examples

[1051] For example, if a user types, "My period started on October 1, 2023. Please predict when my next period will start," the server receives this information and provides it to the generative AI model, which then learns from this new data and more accurately predicts when her next period will start.

[1052] This system allows users to more accurately understand changes in their physical condition and take appropriate measures.

[1053] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1054] Step 1:

[1055] Users input data about changes in their physical condition and menstrual cycle. Specifically, they use a dedicated application or website on their smartphone or computer. For example, a user might enter, "My period started on October 1, 2023." The input data is temporarily stored on the device.

[1056] Step 2:

[1057] The device sends the data entered by the user to the server. The data is encrypted and securely sent to the server. For example, the device encrypts the data "My period started on October 1, 2023" and sends it to the server using the HTTPS protocol. The input is the user's physical condition data, and the output is encrypted data.

[1058] Step 3:

[1059] The server stores the received data in a database. The stored data is organized by user and can be accessed later by the generative AI model. For example, the server stores the data in the database in the format "User ID: 12345, Menstruation Start Date: October 1, 2023". The input is encrypted data, and the output is the data in the format stored in the database.

[1060] Step 4:

[1061] The server provides the stored data to the generative AI model, which then updates the hormone fluctuation prediction model based on this data. For example, the server provides the generative AI model with "User ID: 12345's menstrual start data on October 1, 2023." The input is the data retrieved from the database, and the output is the data provided to the generative AI model.

[1062] Step 5:

[1063] The generative AI model learns from the provided data and updates the hormone fluctuation prediction model. Specifically, it uses a machine learning algorithm to learn new data and improve prediction accuracy. For example, the generative AI model learns from "User ID: 12345's menstrual start data on October 1, 2023" and more accurately predicts the start date of the next menstrual period. The input is the provided data, and the output is an updated prediction model.

[1064] Step 6:

[1065] The generative AI model provides prediction results to the user based on the updated prediction model. The user can check the prediction results through their device. For example, the generative AI model predicts that "User ID: 12345's next menstrual start date is October 29, 2023" and returns this result to the server. The server sends this result to the device, and the user can check it in the "Prediction Results" section of the app. The input is the updated prediction model, and the output is the prediction results provided to the user.

[1066] (Application example 2)

[1067] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1068] Until now, there has been no system that can accurately predict fluctuations in female hormones and the accompanying changes in physical condition, and provide appropriate services based on that prediction. As a result, when women suffer from premenstrual syndrome (PMS), it is difficult for those around them to understand, and it is also difficult for them to recognize it themselves. There has also been a lack of means to provide appropriate electronic payment services in response to changes in physical condition.

[1069] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, and a means for the generation AI to predict hormone fluctuations based on data on the user's physical condition changes and menstrual cycle, and for providing electronic payment services based on the prediction results. This makes it possible to provide optimal electronic payment services in response to changes in the user's physical condition, making it easier for women suffering from PMS to gain understanding from those around them and to recognize the symptoms of PMS themselves.

[1070] A "means for visualizing female hormone fluctuations" is a device or software for visually displaying fluctuations in a user's hormone levels.

[1071] "Means using generative AI" refers to devices or software that use artificial intelligence techniques to analyze data and generate predictive models.

[1072] "Data on changes in the user's physical condition and menstrual cycle" refers to information entered by the user regarding changes in physical condition and menstrual cycle.

[1073] A "means for predicting hormone fluctuations" is a device or software that predicts future fluctuations in hormone levels based on data on changes in the user's physical condition and menstrual cycle.

[1074] "Means for providing electronic payment services" refers to devices or software for providing services related to electronic payments, such as cashback or discount coupons, based on the results of predictions of the user's physical condition and hormone fluctuations.

[1075] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before their period.

[1076] A system for implementing the present invention has the following configuration.

[1077] System configuration

[1078] 1. Hardware

[1079] Server: Stores data and runs generative AI models.

[1080] Terminal: The device (smartphone, tablet, etc.) where users enter data and view results.

[1081] 2. Software

[1082] Generative AI model: A machine learning model that predicts hormone fluctuations based on data on the user's physical condition and menstrual cycle.

[1083] Database: A system for storing user physical condition data and menstrual cycle data.

[1084] Electronic payment service: An application that provides cashback and discount coupons based on the user's physical condition.

[1085] Data processing and calculation

[1086] 1. Data Acquisition

[1087] Users use the device to input data about their physical condition and menstrual cycle, including a physical condition score (1-5) and hormone level score (0-1).

[1088] 2. Data Preprocessing

[1089] The server converts the input data into a numerical format that can be processed by the machine learning model. Specifically, it converts date data into numbers and converts physical condition scores and hormone levels into NumPy arrays.

[1090] 3. Model Update

[1091] The server uses a generative AI model to learn the relationship between physical condition scores and hormone levels, improving the accuracy of hormone fluctuation predictions.

[1092] 4. Prediction

[1093] When a new physical condition score is entered, the server uses a generative AI model to predict hormone levels.

[1094] 5. Offering

[1095] Based on the predicted hormone levels, the server provides users with electronic payment services such as cashback and discount coupons.

[1096] Specific examples

[1097] User A: If the health score is 4 (poor) and the predicted hormone level is 0.4, offer cashback.

[1098] User B: If the health score is 2 (good) and the predicted hormone level is 0.7, offer a discount coupon.

[1099] Prompt Sentence Examples

[1100] "If the user's health score is 4, offer cashback because the predicted hormone level is 0.4."

[1101] In this way, a system can be created in which generative AI predicts hormone fluctuations based on the user's physical condition data and provides optimal electronic payment services.

[1102] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1103] Step 1:

[1104] The user uses the device to input data on changes in physical condition and menstrual cycle. Specifically, the user inputs a physical condition score (1-5) and a hormone level score (0-1). The input data is sent to the server.

[1105] Step 2:

[1106] The server converts the received data into a numerical format and formats it so that the machine learning model can process it. Specifically, it converts date data into numbers and converts the physical condition score and hormone levels into NumPy arrays. The input is the physical condition data from the user, and the output is the formatted data.

[1107] Step 3:

[1108] The server uses a generative AI model to learn the relationship between physical condition scores and hormone levels. Specifically, it uses a Linear Regression model to analyze the relationship between physical condition scores and hormone levels. The input is the formatted data, and the output is the trained model.

[1109] Step 4:

[1110] When a user inputs a new physical condition score, the server uses a generative AI model to predict hormone levels. Specifically, the server inputs the new physical condition score into the model and outputs the predicted hormone levels. The input is the new physical condition score, and the output is the predicted hormone levels.

[1111] Step 5:

[1112] The server provides users with electronic payment services such as cashback or discount coupons based on the predicted hormone levels. Specifically, if the predicted hormone levels are below a certain threshold, the server provides cashback, and if they are above a certain threshold, the server provides discount coupons. The input is the predicted hormone levels, and the output is the electronic payment service provided.

[1113] Example 3

[1114] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1115] Fluctuations in female hormones can cause symptoms such as premenstrual syndrome (PMS), which can have a significant impact on a woman's quality of life. However, there is a lack of means to accurately predict and visualize these hormone fluctuations, making it difficult for women to understand changes in their own physical condition and take appropriate measures. There are also problems with gaining understanding from those around them. This has led to a demand for solutions to women's problems and improving their quality of life.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1117] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generating AI, a means for the user to input information about their menstrual cycle and changes in physical condition via a smartphone, a means for the terminal to transmit the input data to the server, a means for the server to input the received data into a generating AI model, a means for the generating AI model to predict hormone fluctuations, a means for the server to return the prediction results to the terminal, a means for the terminal to visualize the prediction results on the smartphone screen, and a means for the user to confirm the predicted hormone fluctuation results. This allows women to accurately understand their own hormone fluctuations and take appropriate measures. It also makes it easier for them to gain understanding from those around them, which is expected to improve their quality of life.

[1118] A "means for visualizing female hormone fluctuations" is a method or device for visually displaying fluctuations in hormone levels occurring in a woman's body.

[1119] "Means using generative AI" refers to methods or devices that use artificial intelligence technology to analyze data and make predictions or generate results.

[1120] "Means for users to input information about their menstrual cycle and changes in physical condition via smartphone" refers to a method or device that allows users to input data about their menstrual cycle and changes in physical condition using a smartphone application.

[1121] "Means for transmitting data entered by a terminal to a server" refers to a method or device for a terminal such as a smartphone to transmit data entered by a user to a server via the Internet.

[1122] "Means for inputting data received by the server into the generative AI model" refers to a method or device by which the server passes data received from the terminal to the generative AI model for analysis and prediction.

[1123] "Means for a generative AI model to predict hormone fluctuations" refers to a method or device for a generative AI model to predict fluctuations in a woman's hormone levels based on input data.

[1124] "Means for the server to return prediction results to the terminal" refers to a method or device by which the server transmits prediction results obtained by the generative AI model to the terminal.

[1125] "Means for the terminal to visualize the prediction results on the smartphone screen" refers to a method or device for the terminal to display the prediction results received from the server as graphs or charts on the smartphone screen.

[1126] "Means for users to check the predicted results of hormone fluctuations" refers to a method or device that allows users to check the predicted results of hormone fluctuations on the screen of their smartphone.

[1127] This invention is a system for visualizing female hormone fluctuations, which uses a generative AI model to predict hormone fluctuations based on information on a user's menstrual cycle and physical condition changes, and visualizes the results. Specific embodiments of this system are described below.

[1128] Hardware and Software Configuration

[1129] server

[1130] The server provides the computing resources to run the generative AI model. Specifically, it uses advanced natural language processing models such as OpenAI's GPT-4 and Google's BERT as the generative AI model. The server receives data sent by users and inputs it into the generative AI model.

[1131] Device (smartphone)

[1132] The device provides an interface for users to input information about their menstrual cycle and changes in their physical condition. Users input data through a smartphone application, which is then sent to a server via the Internet. The device also visualizes the prediction results received from the server and displays them to the user.

[1133] user

[1134] Users use a smartphone application to input information about their menstrual cycle and changes in physical condition, and the generative AI model then predicts hormone fluctuations and provides the results to the user.

[1135] Data processing and calculation

[1136] Data Entry

[1137] Users enter information such as the start and end dates of their menstrual cycle, body temperature, and mood swings through a smartphone application. For example, they can enter data such as "Menstruation start date: October 1, 2023," "Body temperature: 36.8 degrees," and "Mood: Unstable."

[1138] Data transmission

[1139] The terminal encrypts the input data and sends it to a server via the Internet, thereby maintaining data security.

[1140] Data Analysis and Prediction

[1141] The server inputs the received data into a generative AI model, which then predicts hormone fluctuations based on the input data. For example, it generates prompts like the following:

[1142] "The user's menstrual cycle started on October 1, 2023, and ended on October 7, 2023. Their body temperature fluctuated from 36.5°C to 37.0°C, and their mood changed from unstable to stable. Please use this information to predict their next menstrual cycle and hormone fluctuations."

[1143] Returning and visualizing prediction results

[1144] The server receives the results of the generative AI model's predictions of hormone fluctuations and sends them back to the device. The device then visualizes the received predictions as graphs and charts on the smartphone screen, allowing the user to visually confirm the predicted results of hormone fluctuations.

[1145] Specific examples

[1146] For example, if a user inputs "Period start date: October 1, 2023," "Body temperature: 36.8°C," and "Mood: Unstable," the device sends this data to the server. The server generates a prompt for the generative AI model: "The user's menstrual cycle started on October 1, 2023, and ended on October 7, 2023. Body temperature fluctuated from 36.5°C to 37.0°C, and mood changed from unstable to stable. Based on this information, please predict the next menstrual cycle and hormone fluctuations." This is then input into the model. The generative AI model predicts the start date of the next menstrual cycle and hormone level fluctuations and sends the results back to the server. The server then sends the prediction results back to the device, which visualizes them on the smartphone screen. The user can check the predicted start date of the next menstrual cycle and hormone fluctuations on the screen.

[1147] In this way, the user can accurately understand their own hormone fluctuations and take appropriate measures. In addition, it is easier to gain understanding from those around them, which is expected to improve the quality of life. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1148] Step 1:

[1149] The user launches the smartphone application.

[1150] Specific operation: A user launches the app by tapping the application icon on the smartphone home screen. When the app launches, the login screen is displayed.

[1151] Input: User taps.

[1152] Output: The application login screen is displayed.

[1153] Step 2:

[1154] The user inputs information about their menstrual cycle and changes in physical condition.

[1155] Specific operation: After logging in, the user enters information such as the start and end dates of their menstrual cycle, body temperature, and mood fluctuations into an input form within the application. For example, they might enter data such as "Menstruation start date: October 1, 2023," "Body temperature: 36.8 degrees," and "Mood: Unstable."

[1156] Input: Information about menstrual cycle and changes in physical condition entered by the user.

[1157] Output: The input data is saved in the application.

[1158] Step 3:

[1159] The terminal transmits the input data to the server.

[1160] Specific operation: The terminal encrypts the data entered by the user and sends it to a server via the Internet.

[1161] Input: Information about menstrual cycle and changes in physical condition entered by the user.

[1162] Output: The encrypted data is sent to the server.

[1163] Step 4:

[1164] The server inputs the received data into a generative AI model.

[1165] Specific operation: The server analyzes the data received from the device and inputs it into a generative AI model, such as OpenAI's GPT-4. This model generates prompts to predict hormone fluctuations based on the input data.

[1166] Input: Encrypted data sent from the terminal.

[1167] Output: The prompt sentence that is fed into the generative AI model.

[1168] Step 5:

[1169] Generative AI models predict hormone fluctuations.

[1170] How it works: The generative AI model predicts hormone fluctuations based on a prompt. For example, it might generate a prompt like this: "The user's menstrual cycle starts on October 1, 2023, and ends on October 7, 2023. Her body temperature fluctuated from 36.5°C to 37.0°C, and her mood changed from unstable to stable. Based on this information, please predict her next menstrual cycle and hormone fluctuations."

[1171] Input: The generated prompt statement.

[1172] Output: Predicted hormone fluctuations.

[1173] Step 6:

[1174] The server returns the prediction results to the device.

[1175] Specific operation: The server receives the predicted hormone fluctuation results obtained from the generative AI model and sends them back to the terminal.

[1176] Input: Prediction results from a generative AI model.

[1177] Output: The prediction results are sent to the device.

[1178] Step 7:

[1179] The device visualizes the prediction results on the smartphone screen.

[1180] Specific operation: The device visualizes the received prediction results as graphs and charts on the smartphone screen, allowing users to visually check the predicted results of hormone fluctuations.

[1181] Input: Prediction results sent from the server.

[1182] Output: Graphs and charts displayed on the smartphone screen.

[1183] Step 8:

[1184] The user checks the predicted results of hormone fluctuations.

[1185] How it works: Users can check the predicted hormone fluctuations on their smartphone screen, which will help them predict their next menstrual cycle and changes in their physical condition, and take appropriate measures.

[1186] Input: Prediction results displayed on a smartphone screen.

[1187] Output: Predicted hormone fluctuations confirmed by the user.

[1188] (Application example 3)

[1189] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1190] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), significantly affecting the quality of women's lives. However, it is difficult to accurately grasp these hormone fluctuations and take appropriate measures. There are also problems with gaining understanding from those around you, and with women finding it difficult to recognize PMS themselves. Furthermore, there is a lack of means to provide advice on health management and suggestions for appropriate exercise and diet. To solve these issues, a system is needed that visualizes hormone fluctuations and provides appropriate advice to users.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1192] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generating AI, a means for the user to input information on menstrual cycles and changes in physical condition, a means for the generating AI to predict hormone fluctuations based on this information, a means for visualizing the prediction results, and a means for providing the user with health management advice and appropriate exercise and diet suggestions. This allows the user to accurately understand their own hormone fluctuations and take appropriate measures. It also makes it easier for them to gain understanding from those around them and to recognize PMS themselves. Furthermore, they can receive health management advice and appropriate exercise and diet suggestions.

[1193] "Female hormone fluctuations" refers to the changes in hormones such as estrogen and progesterone that fluctuate periodically within a woman's body.

[1194] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[1195] The "menstrual cycle" refers to the period from the day when menstruation begins to the day when the next menstruation begins in a woman's body.

[1196] "Changes in physical condition" refers to physical or mental changes experienced by the user.

[1197] "Prediction" refers to estimating future conditions based on past data and current information.

[1198] "Visualization" refers to displaying data or information visually to make it easier to understand.

[1199] "Health and Wellness Advice" means specific suggestions or instructions to improve or maintain a user's health and wellness.

[1200] "Appropriate exercise" refers to an exercise program that is tailored to the user's health condition and goals.

[1201] "Meal Suggestions" refers to meal plans and nutritional guidance tailored to a user's health status and goals.

[1202] A system for implementing the present invention is configured as follows.

[1203] System configuration

[1204] 1. User device: A device that allows users to input information about their menstrual cycle and changes in their physical condition. This includes smartphones and tablets.

[1205] 2. Server: A central processing unit that uses a generative AI model to analyze input data from users and predict hormone fluctuations.

[1206] 3. Generative AI model: This is an artificial intelligence model for predicting hormone fluctuations based on user input data. Specifically, it uses the GPT-3 model with the Hugging Face transformers library.

[1207] 4. Visualization tools: Software for visually displaying prediction results. Graph drawing libraries such as Matplotlib are examples of such tools.

[1208] Program processing

[1209] 1. Data entry on the user's device: The user enters information such as the start date of the menstrual cycle, the length of the cycle, and changes in physical condition (e.g., headache, fatigue) through a smartphone application.

[1210] 2. Data analysis on the server: The server receives the data sent by the user and generates prompt sentences to input into the generative AI model.

[1211] 3. Prediction by generative AI model: The generative AI model predicts hormone fluctuations based on the prompt text. The prediction results are returned to the server.

[1212] 4. Visualization: The server uses visualization tools to display the prediction results in a visual format such as a graph, allowing users to intuitively understand their own hormone fluctuations.

[1213] 5. Health management advice: Based on the prediction results, the server provides the user with health management advice and suggestions for appropriate exercise and diet.

[1214] Specific examples

[1215] If the user enters "My period will start on January 1, 2023, my cycle will be 28 days long, and I am experiencing headaches and fatigue," the server will generate the following prompt:

[1216] Example prompt sentence:

[1217] User's cycle started on 2023-01-01. Cycle length is 28 days. Symptoms: headache, fatigue. Predict hormone levels for the next cycle.

[1218] By inputting this prompt into a generative AI model, hormone fluctuations are predicted. The prediction results are visualized as graphs using Matplotlib and displayed to the user. Additionally, advice, exercise, and dietary suggestions based on the user's health status are provided.

[1219] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1220] Step 1:

[1221] Data entry on user terminal

[1222] Through a smartphone application, users input information such as the start date of their menstrual cycle, the length of the cycle, and changes in their physical condition (e.g., headache, fatigue). The input data is temporarily stored within the application and then sent to a server.

[1223] Input: start date of menstrual cycle, cycle length, and information on changes in physical condition

[1224] Output: Sending user-entered data

[1225] Step 2:

[1226] Receiving data and generating prompts on the server

[1227] The server receives data sent from the user's device. Based on the received data, it generates prompt sentences to input into the generative AI model. The prompt sentences are created in a format that includes information about the user's menstrual cycle and changes in physical condition.

[1228] Input: User-entered data

[1229] Output: Generated prompt statement

[1230] Step 3:

[1231] Predicting hormone fluctuations using generative AI models

[1232] The server inputs the generated prompt sentences into the generative AI model, which then predicts hormone fluctuations based on the prompt sentences and returns the results to the server.

[1233] Input: prompt statement

[1234] Output: Hormonal fluctuation prediction results

[1235] Step 4:

[1236] Visualization of prediction results

[1237] The server receives the hormone fluctuation prediction results returned by the generative AI model and displays them in a visual format such as a graph using a visualization tool. The prediction results are visualized using a graph drawing library such as Matplotlib.

[1238] Input: Hormonal fluctuation prediction results

[1239] Output: Visualized graph

[1240] Step 5:

[1241] Providing health management advice

[1242] Based on the visualized prediction results, the server provides the user with health management advice and suggestions for appropriate exercise and diet, which are customized according to the user's health condition and goals.

[1243] Input: Visualized prediction results

[1244] Output: Health care advice, exercise and diet suggestions

[1245] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1246] "Example 1"

[1247] One embodiment of the present invention is a system that uses an emotion engine to recognize a user's emotions. This system recognizes emotions from the user's tone of voice, facial expressions, and choice of words, and feeds this information back to a generation AI. The generation AI combines this emotion information with information about the user's menstrual cycle and changes in physical condition to predict hormone fluctuations. Specifically, if the user feels irritated, they feed that information back to the generation AI, which then uses that information to predict hormone fluctuations.

[1248] "Example 2"

[1249] Another embodiment of the present invention is a system in which an emotion engine captures changes in a user's emotions in real time. In this system, the user takes a picture of their face using a smartphone camera, and the emotion engine recognizes emotions from the video. This emotion information is fed back to the generation AI in real time, and the generation AI updates its predictions of hormone fluctuations based on that information.

[1250] "Example 3"

[1251] Furthermore, another embodiment of the present invention is a system in which an emotion engine records changes in a user's emotions and learns the user's individual emotional patterns based on that data. In this system, the user records their daily emotional changes in an app, and the emotion engine learns from that data. Through this learning, the emotion engine understands the user's individual emotional patterns and provides feedback to the generation AI. The generation AI combines these emotional patterns with information such as menstrual cycles and changes in physical condition to gain a deeper understanding of the relationship between hormonal fluctuations and emotions.

[1252] The processing flow of each embodiment will be described below.

[1253] "Example 1"

[1254] Step 1: The user inputs their tone of voice, facial expressions, choice of words, etc. into the emotion engine.

[1255] Step 2: The emotion engine recognizes the user's emotions from this information and feeds that information back to the generation AI.

[1256] Step 3: The generative AI combines this emotional information with information about the user's menstrual cycle, physical changes, etc. to predict hormone fluctuations.

[1257] "Example 2"

[1258] Step 1: The user takes a picture of their face using their smartphone camera.

[1259] Step 2: The emotion engine recognizes emotions from the captured footage and feeds that information back to the generative AI in real time.

[1260] Step 3: The generative AI updates its predictions of hormone fluctuations based on the feedback information.

[1261] "Example 3"

[1262] Step 1: Users record their daily emotional changes in the app.

[1263] Step 2: The emotion engine learns from the recorded data and understands the individual emotional patterns of the user.

[1264] Step 3: The emotion engine feeds back the emotional patterns it understands to the generation AI. Step 4: The generation AI combines the fed-back emotional patterns with information on menstrual cycles, physical changes, etc. to gain a deeper understanding of the relationship between hormonal fluctuations and emotions.

[1265] Example 1

[1266] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1267] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), which have a major impact on women's lives. However, these symptoms are often difficult for those around them to understand, and it is often difficult for women themselves to recognize them as PMS. For this reason, it is necessary for women to accurately understand their own hormone fluctuations and manage their physical condition. Furthermore, emotional fluctuations are also related to hormone fluctuations, so predictions that take this into account are necessary.

[1268] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1269] In this invention, the server includes: a means for a user to input information about their menstrual cycle and changes in physical condition; a means for a terminal to transmit the input information to the server; a means for the server to store the input information in a database; a means for the server to acquire the user's emotional information using an emotion engine; a means for the server to input the input information and the emotional information to a generative AI model; a means for the generative AI model to predict hormone fluctuations; a means for the server to generate the prediction results as graphs or charts; a means for transmitting the graphs or charts generated by the server to the terminal; and a means for the terminal to display the prediction results to the user. This allows the user to visually check their own hormone fluctuations and the accompanying changes in physical condition, enabling them to manage their physical condition. Furthermore, by taking emotional information into account, more accurate predictions of hormone fluctuations are possible.

[1270] "Female hormone fluctuations" refers to the phenomenon in which the concentration of hormones (such as estrogen and progesterone) secreted in a woman's body changes over time.

[1271] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to make predictions and generate information from data.

[1272] The "menstrual cycle" refers to the periodic occurrence of menstrual periods in a woman's body, usually lasting approximately 28 days.

[1273] "Changes in physical condition" refers to changes in an individual's health or physical condition over time.

[1274] The "emotion engine" is a technology that recognizes emotions from the user's tone of voice, facial expressions, choice of words, etc.

[1275] A "database" is a system for efficiently storing, managing, and retrieving data.

[1276] A "generative AI model" is a machine learning model built to realize generative AI, which makes predictions and generates for specific tasks.

[1277] "Graphs and charts" are diagrams that visually represent data and are used to easily understand trends and patterns in the data.

[1278] A "terminal" is a device used by a user to input information and check results, and includes smartphones, personal computers, etc.

[1279] A "server" is a computer system that processes and stores data on a network.

[1280] This invention is a system that visualizes female hormone fluctuations and solves the problems faced by women suffering from premenstrual syndrome (PMS). The system uses a generative AI model to predict hormone fluctuations based on the user's input of information about their menstrual cycle and changes in physical condition, and then visualizes the results.

[1281] The server receives information on menstrual cycles and changes in physical condition provided by the user and stores it in a database. A relational database such as MySQL or PostgreSQL is used as the database. The server also uses an emotion engine to obtain emotional information from the user's tone of voice, facial expressions, choice of words, etc. A general emotion recognition API is used as the emotion engine.

[1282] The server inputs the input information stored in the database and the emotional information obtained from the emotion engine into a generative AI model. The generative AI model is built using machine learning frameworks such as TensorFlow and PyTorch. This generative AI model predicts hormone fluctuations based on the user's input information.

[1283] The prediction results obtained from the generative AI model are generated as graphs and charts by the server using data visualization libraries such as Matplotlib and Plotly, allowing users to visually confirm hormone fluctuations and the resulting changes in their physical condition.

[1284] For example, if a user inputs "My menstrual cycle is 28 days, and I've been feeling tired lately," the device sends this information to the server. The server stores the information in a database and uses an emotion engine to recognize that the user's tone of voice indicates stress. This information is then input into a generative AI model to predict hormone fluctuations. The prediction results are generated as a graph using Matplotlib and sent to the device. Finally, the device displays the graph to the user, allowing them to check their hormone fluctuations and changes in their physical condition.

[1285] Example prompt sentence:

[1286] Please enter information about the user's menstrual cycle and changes in physical condition. For example, "My menstrual cycle is 28 days, and I've been feeling tired lately." Also, please enter emotional information recognized by the emotion engine. For example, "I can hear stress from the tone of your voice."

[1287] This system allows users to accurately understand their own hormone fluctuations and manage their physical condition. Furthermore, by taking emotional information into account, it becomes possible to predict hormone fluctuations with even greater accuracy.

[1288] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1289] Step 1:

[1290] The user inputs information about their menstrual cycle and changes in physical condition.

[1291] Users use devices such as smartphones or PCs to input information about their menstrual cycle and changes in their physical condition. For example, they might input information such as, "My menstrual cycle is 28 days, and I've been feeling tired lately." The input information is saved in an input form on the device.

[1292] Step 2:

[1293] The terminal sends the input information to the server.

[1294] The terminal sends the information entered by the user to the server. At this time, the data is securely transmitted using the HTTPS protocol. The input information is sent to the server in a data format such as JSON.

[1295] Step 3:

[1296] The server stores the input information in a database.

[1297] The server saves the received user input information in a database, using a relational database such as MySQL or PostgreSQL. The input information is stored in the appropriate table in the database.

[1298] Step 4:

[1299] The server acquires the user's emotion information using an emotion engine.

[1300] The server uses an emotion engine to acquire emotional information from the user's tone of voice, facial expressions, and choice of words. A general emotion recognition API is used as the emotion engine. The acquired emotional information is stored in a temporary data store on the server.

[1301] Step 5:

[1302] The server inputs input information and emotion information into the generated AI model.

[1303] The server inputs the input information stored in the database and the emotion information obtained from the emotion engine into the generative AI model. The generative AI model is built using machine learning frameworks such as TensorFlow and PyTorch. The input information and emotion information are passed to the input layer of the generative AI model.

[1304] Step 6:

[1305] Generative AI models predict hormone fluctuations.

[1306] The generative AI model predicts hormone fluctuations based on input information. The prediction algorithm combines past data and machine learning models to predict hormone fluctuations with high accuracy. The prediction results are obtained from the output layer of the generative AI model.

[1307] Step 7:

[1308] The server generates the prediction results as graphs and charts.

[1309] The server uses data visualization libraries such as Matplotlib and Plotly to generate graphs and charts based on the predictions obtained from the generative AI model. The prediction results are visually represented as graphs and charts.

[1310] Step 8:

[1311] The server sends the generated graphs and charts to the device.

[1312] The server then sends the generated graphs and charts to the device, again securely using the HTTPS protocol. The graphs and charts are sent to the device as image files or in HTML format.

[1313] Step 9:

[1314] The terminal displays the prediction results to the user.

[1315] The device then displays the received graphs and charts to the user, allowing the user to visually confirm hormone fluctuations and the resulting changes in physical condition. The displayed graphs and charts are displayed on the device screen.

[1316] (Application example 1)

[1317] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1318] Fluctuations in female hormones are a problem that is difficult for those around them to understand, especially for women who suffer from PMS (premenstrual syndrome), and it is also difficult for them to recognize it as PMS. There is also a need to accurately predict the changes in physical condition and emotions that accompany hormonal fluctuations and take appropriate measures. Another challenge is providing appropriate purchasing advice to users in physical stores.

[1319] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, and a means for using an emotion engine for recognizing the user's emotions. This makes it possible to solve the problem of women suffering from PMS (premenstrual syndrome), who find it difficult to gain understanding from those around them and to recognize it themselves as PMS. The generation AI can predict hormone fluctuations based on information such as the user's menstrual cycle and physical changes, and visualize the results. Furthermore, the emotion engine can recognize the user's tone of voice and facial expression and provide feedback to the generation AI, thereby improving the accuracy of hormone fluctuation predictions. Additionally, by including a means for providing purchasing advice to users in physical stores, the system can assist users in selecting appropriate products.

[1320] "Female hormone fluctuations" refers to the phenomenon in which the concentrations of hormones such as estrogen and progesterone secreted in a woman's body change over time.

[1321] "Visualization" is the display of data and information in a visual format, such as a graph or chart, to make it easier to understand.

[1322] "Generative AI" is a system that uses artificial intelligence technology to analyze data and generate new information and predictions.

[1323] The "emotion engine" is a system that recognizes emotions from the user's tone of voice, facial expressions, choice of words, etc., and analyzes that information.

[1324] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before menstruation.

[1325] The "menstrual cycle" refers to the period of time until menstruation occurs in a woman's body, which is usually about 28 days.

[1326] "Changes in physical condition" refers to changes in the user's physical condition over time, including, for example, headaches and fatigue.

[1327] "Feedback" is the process by which a system receives input from users and uses that information to improve its behavior and predictions.

[1328] "Prediction accuracy" is a measure of how accurately a system can predict future events or conditions.

[1329] A "brick and mortar store" is a store that exists in a physical location and where customers can visit in person to purchase products.

[1330] "Buying advice" means providing advice or suggestions to help users select appropriate products.

[1331] The system for implementing this invention is configured as follows: First, the server includes a means for visualizing female hormone fluctuations, a means for using a generative AI, and a means for using an emotion engine to recognize the user's emotions. This makes it possible to solve the problem of women who suffer from PMS (premenstrual syndrome), who find it difficult to gain understanding from those around them and who find it difficult to recognize that they are suffering from PMS themselves.

[1332] Hardware and software used

[1333] Hardware: Smartphone (microphone, camera)

[1334] Software: Python, scikit-learn (machine learning library), matplotlib (data visualization library), speech_recognition (speech recognition library), emotion_recognition (emotion recognition library)

[1335] Data processing and calculation

[1336] 1. Collecting User-Entered Information:

[1337] Users use their smartphones to input their menstrual cycle and changes in their physical condition, which allows the server to obtain basic health information about the user.

[1338] 2. Emotion recognition:

[1339] The user's voice is recorded using the smartphone's microphone and analyzed using an emotion recognition library, allowing the server to understand the user's emotional state.

[1340] 3. Predicting hormone fluctuations:

[1341] The server uses the collected data to predict hormone fluctuations using a generative AI model (we'll use Linear Regression here), which then predicts the user's hormone level fluctuations and visualizes them in graphs and charts.

[1342] 4. Visualizing the results:

[1343] The server displays the prediction results in graphs and charts and provides them to the user, allowing them to visually understand their own hormone fluctuations.

[1344] 5. Providing Buying Advice:

[1345] In a physical store, the server provides appropriate purchasing advice to users, allowing them to select products that suit their hormone fluctuations.

[1346] Specific examples

[1347] For example, if a user inputs "My menstrual cycle is 28 days, and I've been having a lot of headaches lately," and emotion recognition detects "stress," the server will use generative AI to predict hormone fluctuations and, based on the prediction results, provide advice on purchasing specific supplements during periods of increased stress.

[1348] Prompt Sentence Examples

[1349] The user has a 28-day menstrual cycle and has recently been experiencing a lot of headaches. Emotion recognition detects stress. Using this information, the generative AI predicts hormone fluctuations and recommends purchasing specific supplements during periods of increased stress.

[1350] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1351] Step 1:

[1352] The user uses a smartphone to input their menstrual cycle and changes in their physical condition. Specifically, they input the number of days in their menstrual cycle and recent changes in their physical condition (e.g., headache, fatigue, etc.) into the application's input form. The input data is sent to the server. The input data is text information about the number of days in their menstrual cycle and changes in their physical condition.

[1353] Step 2:

[1354] The server stores the received user input data and then collects voice data for emotion recognition. The user records their own voice using the microphone on their smartphone. The recorded voice data is sent to the server. The input data is an audio file, and the output data is voice data for emotion recognition.

[1355] Step 3:

[1356] The server uses an emotion recognition library (emotion_recognition) to analyze the user's emotions from the received voice data. Specifically, it analyzes the voice data and identifies emotions from the user's tone of voice and choice of words. The input data is an audio file, and the output data is emotional information (e.g., stress, joy, etc.).

[1357] Step 4:

[1358] The server uses a generative AI model (Linear Regression) to predict hormone fluctuations based on the user's menstrual cycle, physical changes, and emotional information. Specifically, this data is input into the model to predict hormone level fluctuations. The input data is the number of days in the menstrual cycle, text information on physical changes, and emotional information, and the output data is the predicted hormone fluctuation results.

[1359] Step 5:

[1360] The server visualizes the prediction results in graphs and charts and provides them to users. Specifically, the matplotlib library is used to visually display the prediction results. The input data is the predicted results of hormone fluctuations, and the output data is graphs and charts.

[1361] Step 6:

[1362] The server provides purchasing advice to users in physical stores. Specifically, it recommends appropriate products (e.g., supplements) to users based on the prediction results. The input data is the predicted hormone fluctuation results, and the output data is purchasing advice.

[1363] Example 2

[1364] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1365] Fluctuations in female hormones can cause symptoms such as premenstrual syndrome (PMS), which can be a significant burden for women. However, these symptoms are often difficult to understand from those around them, and it is often difficult for women themselves to recognize them as PMS. For this reason, it is necessary for women to accurately understand changes in their own physical condition and take appropriate measures. Furthermore, it is necessary to predict hormone fluctuations that take into account individual changes in physical condition and emotional fluctuations, but the current system does not have sufficient accuracy.

[1366] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1367] In this invention, the server includes: a means for a user to input data on changes in physical condition and menstrual cycle; a means for a terminal to transmit the input data to the server; a means for the server to store the data in a database; a means for the server to train a generative AI model using the stored data; a means for a user to take a picture of their face using a camera; a means for the terminal to transmit video data to the server; a means for the server to recognize emotions from the video using an emotion engine; a means for the server to feed back emotional information to the generative AI model; and a means for the server to update the generative AI model and improve the accuracy of hormone fluctuation predictions. This allows women to accurately understand their own changes in physical condition and to gain understanding from those around them, and enables highly accurate predictions of hormone fluctuations that take into account individual changes in physical condition and emotional fluctuations.

[1368] "Female hormone fluctuations" refers to the phenomenon in which the amount and balance of hormones secreted in a woman's body change over time.

[1369] "Generative AI" is a system that uses artificial intelligence technology to generate new information and predictions from data.

[1370] "Changes in physical condition" refers to changes in the user's physical condition, including symptoms such as headaches and fatigue.

[1371] The "menstrual cycle" refers to the periodic cycle in which a woman's menstrual cycle repeats, usually about 28 days.

[1372] A "terminal" is a device that allows a user to input data or use a camera, and includes smartphones and personal computers.

[1373] A "server" is a computer system that stores and processes data, trains generative AI models, and so on.

[1374] A "database" is a system for systematically storing and managing collected data.

[1375] "Training" is the process by which a generative AI model learns from data and improves its prediction accuracy.

[1376] "Camera" means a device for photographing a user's face, including those built into smartphones and personal computers.

[1377] The "emotion engine" is a software system that recognizes user emotions from video data.

[1378] "Feedback" is the process of providing new data or information to a generative AI model to improve its accuracy.

[1379] "Hormonal fluctuation prediction accuracy" is an indicator of how accurately the generative AI model can predict female hormone fluctuations.

[1380] This invention is a system that visualizes female hormone fluctuations and improves the accuracy of hormone fluctuation predictions using generative AI. Specific embodiments of this system are described below.

[1381] First, the user uses a device such as a smartphone or computer to input data about their physical condition and menstrual cycle. For example, they enter information such as "I have a headache" or "the first day of my period" into a dedicated application. This application is designed to make it easy for users to input data.

[1382] The device then sends the entered data to the server in real time, using the HTTPS protocol to ensure data security. The server then stores the received data in a database, typically a relational database such as MySQL or PostgreSQL.

[1383] The server uses the stored data to train the generative AI model. During this training process, the data is preprocessed using Python's Pandas library, features are extracted using Scikit-learn, and the model is trained using TensorFlow. This gives the generative AI model the ability to predict hormone fluctuations based on the user's physical condition and menstrual cycle.

[1384] The user then takes a photo of their face using the smartphone camera, following the instructions of a dedicated application. The device then sends the captured video data to a server in real time. This transmission also uses the HTTPS protocol to ensure data security.

[1385] The server inputs the received video data into an emotion engine to recognize the user's emotions. The emotion engine is developed using facial recognition libraries such as OpenCV and Dlib. The recognized emotion information is fed back to the generative AI model to improve the model's prediction accuracy.

[1386] As a concrete example, consider the case where a user inputs "I have a headache" and takes a photo of their face using their smartphone camera. The data "I have a headache" input by the user is sent from the device to the server and stored in a database. Next, the video data of the user's face is sent to the server, and the emotion engine recognizes the emotion "sad." This emotion information is fed back to the generative AI model, improving the model's prediction accuracy.

[1387] Example prompt sentence:

[1388] "If a user inputs 'I have a headache' as a symptom of their physical condition and takes a picture of their face using their smartphone camera, the emotion engine will recognize the emotion 'sad' from the video. Based on this information, the generative AI model will update its predictions of hormone fluctuations."

[1389] In this way, a system is created in which servers, terminals, and users work together to collect and process data, improving the predictive accuracy of generative AI models.

[1390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1391] Step 1:

[1392] The user inputs data on changes in physical condition and menstrual cycle.

[1393] Users use a dedicated application on their smartphone or computer to input data about their physical condition and menstrual cycle. For example, they input information such as "I have a headache" or "the first day of my period." The input data is entered into a form in the application, and by pressing the submit button, they proceed to the next step.

[1394] Step 2:

[1395] The terminal sends the input data to the server.

[1396] The device sends the data entered by the user to the server in real time. This transmission uses the HTTPS protocol to ensure data security. The input data is sent to the server in JSON format. For example, it is sent in the format {"Physical condition change": "I have a headache", "Menstruation start date": "2023-10-01"}.

[1397] Step 3:

[1398] The server stores the data in a database.

[1399] The server saves the received data in a database. A relational database such as MySQL or PostgreSQL is used for the database. The server parses the received JSON data and saves it in the appropriate table. For example, data on changes in physical condition is saved in a "Physical Condition Changes" table, and data on menstrual cycle is saved in a "Menstrual Cycle" table.

[1400] Step 4:

[1401] The server uses the stored data to train a generative AI model.

[1402] The server uses the stored data to train a generative AI model. This training process involves preprocessing the data using Python's Pandas library, extracting features using Scikit-learn, and training the model using TensorFlow. Input data is retrieved from the database, preprocessed, and then fed into the model. The output is an updated generative AI model.

[1403] Step 5:

[1404] The user takes a picture of their face using the smartphone camera.

[1405] The user takes a picture of their face using the smartphone camera, following the instructions of a dedicated application. The captured image data is then sent to the server in the next step.

[1406] Step 6:

[1407] The terminal transmits the video data to the server.

[1408] The device sends the captured video data to the server in real time. This transmission also uses the HTTPS protocol to ensure data security. The video data is sent to the server in MP4 format, for example.

[1409] Step 7:

[1410] The server uses an emotion engine to recognize emotions from the video.

[1411] The server inputs the received video data into the emotion engine to recognize the user's emotion. The emotion engine is developed using a face recognition library such as OpenCV or Dlib. The input data is the video data, and the output is the recognized emotion information (e.g., "sad").

[1412] Step 8:

[1413] The server feeds emotional information back to the generative AI model.

[1414] The server feeds the recognized emotional information back to the generative AI model. This feedback allows the generative AI model to predict hormone fluctuations taking into account the user's emotional changes. The input data is emotional information, and the output is an updated generative AI model.

[1415] Step 9:

[1416] The server updates the generated AI model to improve the accuracy of predicting hormone fluctuations.

[1417] The server updates the generative AI model based on the fed-back emotional information. This update improves the accuracy of hormone fluctuation predictions, making it possible to more accurately predict changes in the physical condition of each individual user. The input data is emotional information and physical condition change data, and the output is a highly accurate prediction of hormone fluctuations.

[1418] (Application example 2)

[1419] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1420] Conventional hormone fluctuation prediction systems can predict hormone fluctuations based on the user's physical condition and menstrual cycle, but they cannot reflect the user's emotional state in real time, which means that the prediction accuracy is insufficient. In addition, they cannot suggest appropriate payment methods according to the user's emotional state, which makes it difficult to improve user convenience. There is a need to solve these problems.

[1421] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1422] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for recognizing a user's emotional state in real time, a means for updating a hormone fluctuation prediction model based on the user's emotional state, and a means for proposing an optimal payment method according to the user's emotional state. This makes it possible to predict hormone fluctuations that reflect the user's emotional state in real time, and further to propose an optimal payment method according to the user's emotional state.

[1423] "Means for visualizing female hormone fluctuations" refers to devices or software that visually display female hormone fluctuations based on the user's physical condition data and menstrual cycle data.

[1424] "Means using generative AI" refers to devices or software that use artificial intelligence technology to analyze a user's physical condition data and emotional data, and generate and update predictive models of hormone fluctuations.

[1425] "Means for recognizing a user's emotional state in real time" refers to devices or software that use cameras or sensors to acquire a user's facial expressions and biometric data, and then use emotion recognition algorithms to determine the user's emotions in real time.

[1426] The "means for updating the hormone fluctuation prediction model based on the user's emotional state" refers to a device or software for dynamically correcting and updating the hormone fluctuation prediction model based on the user's emotional data recognized in real time.

[1427] "Means for proposing the optimal payment method according to the user's emotional state" refers to devices or software that analyze the user's emotional state, select the payment method that is most suitable for that state, and propose it to the user.

[1428] A system for implementing the present invention is configured as follows.

[1429] First, the server is equipped with a means for visualizing female hormone fluctuations. This means is a device or software that visually displays female hormone fluctuations based on the user's physical condition data and menstrual cycle data. Specifically, it collects data entered by the user through a smartphone or wearable device and displays it as graphs and charts.

[1430] Next, the server is equipped with a means for using generative AI. This means is a device or software that uses artificial intelligence technology to analyze the user's physical condition data and emotional data and generate and update a prediction model of hormone fluctuations. Specifically, it uses machine learning libraries such as Keras and TensorFlow to analyze the user's data and build a prediction model of hormone fluctuations.

[1431] Furthermore, the server is equipped with a means for recognizing the user's emotional state in real time. This means is a device or software that uses a camera or sensor to capture the user's facial expressions and biometric data, and determines the user's emotions in real time using an emotion recognition algorithm. Specifically, it uses OpenCV to detect faces and an emotion recognition model to determine the user's emotions.

[1432] The server also has a means for updating the hormone fluctuation prediction model based on the user's emotional state. This means is a device or software that dynamically corrects and updates the hormone fluctuation prediction model based on the user's emotional data recognized in real time. This enables more accurate hormone fluctuation prediction that reflects the user's emotional state.

[1433] Finally, the server is equipped with a means for suggesting the optimal payment method according to the user's emotional state. This means is a device or software that analyzes the user's emotional state, selects the payment method that best suits that state, and suggests it to the user. For example, if the user is feeling stressed, it suggests one-touch payment, and if the user is relaxed, it suggests point-back payment.

[1434] For example, a user can take a photo of their face using their smartphone camera, and the emotion engine will recognize their emotions from the video. This emotional information is fed back to the generation AI in real time, and the generation AI will use this information to update its predictions of hormone fluctuations. Furthermore, the system will suggest the most appropriate payment method based on the user's emotional state.

[1435] Examples of prompts include:

[1436] “Recognize the user’s emotional state in real time and update your hormone fluctuation prediction model. If the user is stressed, suggest one-touch payment, and if they are relaxed, suggest point-back payment.”

[1437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1438] Step 1:

[1439] The user activates the smartphone camera and takes a picture of their face.

[1440] Input: User's face video

[1441] Output: Facial video data

[1442] Specific operation: The user launches the camera app on their smartphone and takes a picture of their face. The camera captures the facial image data.

[1443] Step 2:

[1444] The device acquires facial image data and detects the face using OpenCV.

[1445] Input: Facial image data

[1446] Output: Face region data

[1447] Specific operation: The device inputs face video data into the OpenCV library and runs the face detection algorithm, thereby determining the position and size of the face and generating face region data.

[1448] Step 3:

[1449] The terminal inputs the facial region data into an emotion recognition model to determine the user's emotion.

[1450] Input: Face region data

[1451] Output: Emotion data

[1452] Specific operation: The device inputs the facial region data into the emotion recognition model built with Keras and executes the emotion recognition algorithm, thereby determining the user's emotion (e.g., stress, relaxation, etc.) and generating emotion data.

[1453] Step 4:

[1454] The device sends emotional data to a server, and the generative AI model is used to update the hormone fluctuation prediction model.

[1455] Input: Emotion data

[1456] Output: Updated hormone fluctuation prediction model

[1457] Specific operation: The device sends emotional data to the server, which then uses the generated AI model to update the hormone fluctuation prediction model, thereby generating an updated hormone fluctuation prediction model that reflects the user's emotional state.

[1458] Step 5:

[1459] Based on the updated hormone fluctuation prediction model, the server proposes the optimal payment method according to the user's emotional state.

[1460] Input: Updated hormone fluctuation prediction model

[1461] Output: Best payment method suggestions

[1462] Specific operation: The server analyzes the updated hormone fluctuation prediction model and selects the payment method (e.g., one-touch payment, point-back payment, etc.) that best suits the user's emotional state. The selected payment method is then proposed to the user.

[1463] Step 6:

[1464] The user reviews and selects the proposed payment method.

[1465] Input: Best payment method suggestions

[1466] Output: Selected payment method

[1467] Specific operation: The user reviews the payment method suggestions displayed on the smartphone screen and selects the payment method that best suits them. The selected payment method is recorded in the system.

[1468] Example 3

[1469] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1470] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), significantly affecting the quality of women's lives. However, it is difficult to accurately grasp and predict these hormone fluctuations and the accompanying changes in physical condition and emotions. In addition, women who suffer from PMS often find it difficult to gain understanding from those around them, and even to recognize it as PMS themselves. To solve these issues, a system is needed that visualizes the relationship between hormone fluctuations and emotions and improves prediction accuracy.

[1471] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for collecting information on menstrual cycles and changes in physical condition input by the user, a means for the generation AI to predict hormone fluctuations based on this information, a means for visualizing the prediction results and displaying them to the user, a means for the user to input daily emotional changes, a means for an emotion engine to learn the user's emotional data and understand emotional patterns, a means for feeding back the emotional patterns to the generation AI, and a means for the generation AI to reevaluate the relationship between hormone fluctuations and emotions and improve prediction accuracy. This allows the user to accurately understand the relationship between their own hormone fluctuations and emotions and predict and manage symptoms such as PMS.

[1472] "Means for visualizing female hormone fluctuations" refers to technology for displaying fluctuations in a user's hormone levels in a visual format such as graphs or charts.

[1473] "Means using generative AI" refers to methods that use artificial intelligence technology to analyze data and make predictions and judgments.

[1474] "Means for collecting information on menstrual cycles and changes in physical condition entered by users" refers to technology for collecting data on menstrual cycles and changes in physical condition entered by users via smartphones or other devices.

[1475] "The means by which generative AI predicts hormone fluctuations based on this information" refers to artificial intelligence technology that analyzes collected data on menstrual cycles and changes in physical condition to predict fluctuations in hormone levels.

[1476] "Means for visualizing and displaying predicted results to users" refers to technology for displaying the results of hormone fluctuations predicted by the generative AI to users in a visual format such as graphs or charts.

[1477] "Means for users to input their daily emotional changes" refers to an interface that allows users to input their daily emotional changes through a smartphone or other device.

[1478] "Means for the emotion engine to learn the user's emotional data and understand emotional patterns" refers to technology that analyzes the emotional data entered by the user and learns the patterns of change in the user's emotions.

[1479] "Means for feeding back emotional patterns to the generative AI" refers to a method in which the emotion engine provides the learned emotional patterns to the generative AI, allowing the generative AI to improve its prediction accuracy based on that information.

[1480] "Means for generative AI to reevaluate the relationship between hormone fluctuations and emotions and improve prediction accuracy" refers to a technology in which generative AI combines and analyzes data on emotional patterns and hormone fluctuations to improve the accuracy of hormone fluctuation predictions.

[1481] This invention is a system that visualizes female hormone fluctuations and predicts and manages the relationship between hormone fluctuations and emotions using generative AI. Specific embodiments of this system are described below.

[1482] First, the user uses a smartphone application to input information about their menstrual cycle and changes in their physical condition. This application provides a user interface and is designed to allow users to easily input data. The input data is then sent from the device (smartphone) to the server. The communication is encrypted and uses a secure protocol (e.g., HTTPS).

[1483] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-4). The generative AI model analyzes data on the user's menstrual cycle and physical changes to predict hormone fluctuations. The prediction results are sent from the server to the device, which visualizes them as graphs and charts and displays them to the user. The user can view this information on the app's dashboard.

[1484] Additionally, users input their daily emotional changes into the app. This emotional data is also sent from the device to the server. The server then uses an emotion engine to analyze the emotional data and learn the user's emotional patterns. The emotion engine understands the user's emotional changes and feeds those patterns back into the generative AI model.

[1485] The generative AI model combines emotional patterns with data on menstrual cycles and physical changes to reassess the relationship between hormone fluctuations and emotions. This improves the accuracy of hormone predictions. Ultimately, users will be able to accurately understand the relationship between their own hormone fluctuations and emotions, and predict and manage symptoms such as PMS.

[1486] Examples:

[1487] The user enters "Menstruation start date: October 1, 2023," "Physical condition: Easily tired," and "Emotions: Irritable" into a smartphone app. The device sends this data to the server. The server passes the data to the generative AI model, which predicts hormone fluctuations. The generative AI model predicts "next expected menstruation date: October 29, 2023," and "Hormonal fluctuations: High stress period," and returns the results to the server. The server sends the prediction results to the device, which displays them as graphs. The user can check this information on the app's dashboard.

[1488] Example prompt sentence:

[1489] "Please predict the next menstrual period and hormone fluctuations based on the menstrual cycle and physical condition change data entered by the user. Also, please take into account the emotional data and predict emotional changes."

[1490] In this way, the server, terminal, and user each play their respective roles, and the system utilizes the generation AI and emotion engine to visualize the relationship between hormone fluctuations and emotions. The flow of the identification process in Example 3 will be described with reference to Fig. 21.

[1491] Step 1:

[1492] The user inputs information about their menstrual cycle and changes in physical condition.

[1493] Users open the smartphone app and enter the start date of their period and any changes in their physical condition (e.g., fatigue, headaches, etc.). The entered data is saved on the user's smartphone.

[1494] Step 2:

[1495] The terminal sends the input data to the server.

[1496] The device sends the data entered by the user to the server in real time. The data is encrypted and uses a secure communication protocol (e.g., HTTPS). The input data includes information about the start date of menstruation and changes in physical condition. The server receives this data and stores it in a database.

[1497] Step 3:

[1498] The server passes the data to a generative AI model to predict hormone fluctuations.

[1499] The server passes the received data to a generative AI model (e.g., OpenAI's GPT-4). The generative AI model analyzes data on the user's menstrual cycle and changes in physical condition to predict hormone fluctuations. The input data is information about the start date of menstruation and changes in physical condition, and the output data is the expected date of the next menstrual period and predicted fluctuations in hormone levels.

[1500] Step 4:

[1501] The server sends the prediction results to the device.

[1502] The server sends predictions from the generative AI model, including the expected date of the next menstrual period and hormone level fluctuations, to the device, which receives this data and prepares it for display to the user.

[1503] Step 5:

[1504] The device visualizes the prediction results and displays them to the user.

[1505] The device visualizes the received prediction results as graphs and charts and displays them to the user. The user can view this information on the app's dashboard. The input data is the prediction results, and the output data is the visually displayed graphs and charts.

[1506] Step 6:

[1507] Users input their daily emotional changes.

[1508] Users input their daily emotional changes (e.g., irritation, sadness, etc.) through the app. The input data is information about the user's emotions. This data is stored on the user's smartphone.

[1509] Step 7:

[1510] The device transmits the emotion data to the server.

[1511] The device sends the emotion data entered by the user to the server. The data is encrypted and uses a secure communication protocol. The input data is information about the user's emotions, and the server receives this data and stores it in a database.

[1512] Step 8:

[1513] The server passes the data to the emotion engine, which learns the emotion patterns.

[1514] The server passes the received emotion data to the emotion engine, which analyzes the data and learns the user's emotion patterns. The input data is information about the user's emotions, and the output data is the learned emotion patterns.

[1515] Step 9:

[1516] The server feeds back the emotional patterns to the generative AI model.

[1517] The server feeds back the emotional patterns obtained from the emotion engine to the generative AI model. The generative AI model reevaluates the relationship between hormone fluctuations and emotions based on this feedback. The input data are the learned emotional patterns, and the output data are the reevaluated predictions of hormone fluctuations.

[1518] Step 10:

[1519] The generative AI model reassessed the relationship between hormone fluctuations and emotions to improve prediction accuracy.

[1520] The generative AI model combines and analyzes emotional patterns with data on menstrual cycles and changes in physical condition, and reevaluates the relationship between hormone fluctuations and emotions. This improves the accuracy of hormone fluctuation predictions. The input data are emotional patterns and data on menstrual cycles and changes in physical condition, and the output data is improved prediction accuracy.

[1521] (Application example 3)

[1522] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1523] Fluctuations in female hormones and the accompanying changes in physical condition and emotions are extremely important information for each woman, but accurately understanding this information and taking appropriate measures can be difficult. Women who suffer from PMS (premenstrual syndrome) often struggle to gain understanding from those around them, and even to recognize it as PMS themselves. Furthermore, it is not easy to select the most appropriate health products and supplements based on this information. A system to solve these problems is needed.

[1524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1525] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for inputting a user's menstrual cycle, physical condition changes, and emotional data, a means for the generation AI to analyze the input data and predict hormone fluctuations, a means for an emotion engine to learn the user's emotional patterns, and a means for suggesting optimal health products and supplements to the user based on the prediction results. This enables the user to accurately understand their own hormone fluctuations and emotional patterns and select optimal health products and supplements.

[1526] "Female hormone fluctuations" refers to the fluctuations in hormones such as estrogen and progesterone that change periodically in a woman's body.

[1527] "Visualization" refers to displaying data or information visually to make it easier to understand.

[1528] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and make predictions and generate results.

[1529] The "menstrual cycle" refers to the period from the first day of a woman's menstrual period to the first day of her next menstrual period.

[1530] "Changes in physical condition" refers to changes in an individual's health or physical condition.

[1531] "Emotional data" refers to data that records a user's emotional state and fluctuations.

[1532] An "emotion engine" refers to a system that analyzes users' emotional data and learns their emotional patterns.

[1533] "Health Products" refers to products used to maintain or improve health.

[1534] "Supplements" refers to products such as vitamins and minerals that are taken as dietary supplements.

[1535] "PMS (premenstrual syndrome)" refers to the general term for the physical and psychological symptoms that women experience before their period.

[1536] This invention is a system that visualizes female hormone fluctuations and uses generative AI to analyze a user's menstrual cycle, physical condition changes, and emotional data. A specific embodiment of this system is described below.

[1537] System configuration

[1538] The system consists of the following main components:

[1539] 1. User device: A mobile device such as a smartphone or tablet.

[1540] 2. Server: A cloud server for data analysis and running generative AI models.

[1541] 3. Database: A database for storing the user's menstrual cycle, physical condition changes, and emotional data.

[1542] Hardware and software used

[1543] Hardware: Smartphone, cloud server

[1544] Software: Python, TensorFlow, Scikit-learn

[1545] Data Entry

[1546] The user enters the following data via a smartphone application:

[1547] Menstrual cycle data

[1548] Data on changes in physical condition

[1549] Emotional Data

[1550] Data analysis

[1551] The server receives the data sent by the user and performs the following operations:

[1552] 1. Analysis using a generative AI model: Using a generative AI model, we analyze menstrual cycle and physical condition change data to predict hormone fluctuations.

[1553] 2. Learning by emotion engine: The emotion engine analyzes the user's emotion data and learns emotional patterns.

[1554] Visualizing the results

[1555] The analysis results are visualized on a smartphone screen, allowing users to check their own hormone fluctuations and emotional patterns at any time.

[1556] Product suggestion

[1557] Based on the analysis, the server will suggest optimal health products and supplements to the user, based on the user's hormonal fluctuations and emotional patterns.

[1558] Specific examples

[1559] For example, if a user enters the following data:

[1560] Menstrual cycle data: 28 days, 30 days, 27 days, 29 days

[1561] Physical condition change data: 5, 6, 4, 5

[1562] Emotion data: 3, 4, 2, 3

[1563] Based on this data, the generative AI predicts hormone fluctuations, and the emotion engine learns emotional patterns, ultimately suggesting "vitamin C supplements" to the user.

[1564] Prompt Sentence Examples

[1565] Enter the user's menstrual cycle data, physical condition data, and emotional data. Generative AI predicts hormone fluctuations based on this data, and the emotional engine learns emotional patterns. Based on the prediction results, it recommends optimal health products and supplements.

[1566] This way, users can easily find the products that best suit their health condition.

[1567] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1568] Step 1:

[1569] The user starts the smartphone application and inputs menstrual cycle data, physical condition change data, and emotion data.

[1570] Input: menstrual cycle data, physical condition change data, emotional data

[1571] Output: Sending input data

[1572] Specific behavior: When a user enters data into the app's input form and presses the "Submit" button, the data is sent to the server.

[1573] Step 2:

[1574] The server receives the data sent by the user and stores it in a database.

[1575] Input: Menstrual cycle data, physical condition change data, and emotional data sent by the user

[1576] Output: Data stored in the database

[1577] Specific operation: The server analyzes the received data, converts it into an appropriate format, and saves it in the database.

[1578] Step 3:

[1579] The server uses the generated AI model to analyze menstrual cycle data and physical condition change data and predict hormone fluctuations.

[1580] Input: Menstrual cycle data and physical condition change data stored in the database

[1581] Output: Predicted hormone fluctuations

[1582] How it works: The server runs the generative AI model and predicts hormone fluctuations based on the input data. The prediction results are temporarily stored in memory.

[1583] Step 4:

[1584] The server uses an emotion engine to analyze the emotion data and learn the user's emotion patterns.

[1585] Input: Emotion data stored in a database

[1586] Output: Learning result of emotion patterns

[1587] Specific operation: The server runs the emotion engine and learns the user's emotion patterns based on the emotion data. The learning results are temporarily stored in memory.

[1588] Step 5:

[1589] The server integrates the results of hormone fluctuation predictions and learning of emotional patterns to suggest optimal health products and supplements to users.

[1590] Input: Predicted hormone fluctuations, learning results of emotional patterns

[1591] Output: Health product and supplement suggestions

[1592] Specific operation: Based on the integrated data, the server selects the health products and supplements that are best suited to the user and generates recommendations.

[1593] Step 6:

[1594] The server sends the proposal to the user's smartphone, and the user confirms the proposal.

[1595] Input: Proposal content for health products and supplements

[1596] Output: Suggestions displayed on the user's smartphone

[1597] Specific operation: The server sends the proposal to the user's smartphone, and the app displays the proposal. The user can check the proposal and purchase the product if necessary.

[1598] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1599] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1600] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1601] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1602] [Third embodiment]

[1603] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1604] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1605] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1606] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1607] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1608] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1609] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[1610] The transfer is done securely.

[1611] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1612] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1613] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1614] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1615] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1616] "Example 1"

[1617] One embodiment of the present invention is a female hormone fluctuation visualization system that uses generative AI. In this system, generative AI predicts hormone fluctuations based on information input by the user, such as information on menstrual cycles and changes in physical condition. The predicted results are presented to the user in the form of graphs or charts, visualizing hormone fluctuations and the accompanying changes in physical condition.

[1618] "Example 2"

[1619] Furthermore, the Generative AI learns from user feedback. Specifically, users input actual data on changes in their physical condition and menstrual cycle into the system, and the Generative AI updates the hormone fluctuation prediction model based on that data. This improves the accuracy of hormone fluctuation predictions, making it possible to more accurately predict changes in each user's physical condition.

[1620] "Example 3"

[1621] In another embodiment of the present invention, a female hormone fluctuation visualization system using generative AI is implemented as a smartphone application. Users input information about their menstrual cycle and changes in their physical condition via their smartphone, and the generative AI predicts hormone fluctuations based on this information. The predicted results are visualized on the smartphone screen, allowing users to check their own hormone fluctuations at any time.

[1622] The processing flow of each embodiment will be described below.

[1623] "Example 1"

[1624] Step 1: The user enters information about their menstrual cycle and physical condition into the system.

[1625] Step 2: Generative AI predicts hormone fluctuations based on input information.

[1626] Step 3: Present the prediction results to the user in the form of graphs, charts, etc., to visualize hormone fluctuations and the resulting changes in physical condition.

[1627] "Example 2"

[1628] Step 1: The user enters actual data about their physical condition and menstrual cycle into the system.

[1629] Step 2: The generative AI updates the hormone fluctuation prediction model based on the input data.

[1630] Step 3: Using the updated prediction model, the accuracy of predicting hormone fluctuations is improved, allowing for more accurate prediction of changes in each user's physical condition.

[1631] "Example 3"

[1632] Step 1: The user inputs information about their menstrual cycle and changes in physical condition via their smartphone. Step 2: The generative AI predicts hormone fluctuations based on this information.

[1633] Step 3: The prediction results are visualized on the smartphone screen, allowing users to check their own hormone fluctuations at any time.

[1634] Example 1

[1635] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1636] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS), significantly impacting the quality of women's lives. However, there is a lack of means to accurately predict and visualize these hormone fluctuations and the accompanying changes in physical condition, making it difficult for women themselves to understand their own changes in physical condition, and for those around them to understand them. Furthermore, existing systems face the challenge of making it difficult to effectively utilize user feedback to improve prediction accuracy.

[1637] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1638] In this invention, the server includes: a means for the user to provide input information through a terminal; a means for the server to receive the input information and organize the data; a means for the server to send prompts to the generative AI model; a means for the generative AI model to predict hormone fluctuations; a means for the server to receive the prediction results and organize the data; a means for the server to visualize the prediction results as graphs or charts; a means for the server to send the visualization results to the user's terminal; and a means for the user to check the visualization results on the terminal. This allows the user to visually understand their own hormone fluctuations and the accompanying changes in their physical condition, making it easier for others to understand. Furthermore, the generative AI model can improve the accuracy of hormone fluctuation predictions by learning from user feedback.

[1639] "User" refers to an individual who uses the system to provide information about their menstrual cycle and changes in physical condition.

[1640] "Terminal" refers to the electronic device used by the user to input information and check the results. Specifically, this includes smartphones and personal computers.

[1641] "Server" refers to the computer system responsible for receiving input information from users, processing the data, and sending it to the generative AI model.

[1642] "Generative AI model" refers to an artificial intelligence model that predicts hormone fluctuations based on data provided by the user.

[1643] "Prompt sentence" refers to an instruction sent to a generative AI model to make a prediction.

[1644] "Hormonal fluctuations" refers to the fluctuations in hormone levels, such as estrogen and progesterone, that occur during a woman's menstrual cycle.

[1645] "Organizing data" refers to converting user-provided information into an appropriate format that makes it easier to use for analysis and prediction.

[1646] "Visualization" refers to displaying the predicted hormone fluctuation results in a visual format such as a graph or chart.

[1647] "Feedback" refers to additional or corrective information provided by users that is used to improve the predictive accuracy of generative AI models.

[1648] This invention is a system for visualizing female hormone fluctuations using a generative AI model. Specific embodiments of this system are described below.

[1649] First, the user provides information about their menstrual cycle and changes in their physical condition through a device. The device can be a common electronic device such as a smartphone or PC. The user enters the following data through a dedicated application or web form:

[1650] Menstrual cycle data: [28, 30, 27, 29]

[1651] Physical condition change data: ["headache", "fatigue", "depression"]

[1652] The server then receives the data sent by the user and processes it using the Python Pandas library to organize the data into a data frame, which converts the data into a format that is easy to use for analysis and prediction.

[1653] The server then sends the organized data to a generative AI model, such as OpenAI's GPT-4, which is an advanced artificial intelligence model. The server then generates a prompt like the following and sends it to the generative AI model:

[1654] User's menstrual cycle data: [28, 30, 27, 29]

[1655] User's physical condition data: ["headache", "fatigue", "depression"]

[1656] Use this data to predict hormone fluctuations during your next menstrual cycle.

[1657] The generative AI model predicts hormone fluctuations based on the prompt text sent, generating predictions such as the following:

[1658] The hormonal fluctuations during your next menstrual cycle are as follows:

[1659] Day 1: Estrogen levels drop

[1660] Day 5: Progesterone levels rise

[1661] Day 14: Estrogen levels peak

[1662] Day 21: Progesterone levels peak

[1663] The server receives the prediction results returned by the generative AI model and organizes them as a data frame using Pandas. The organized prediction results are then visualized as graphs and charts using the Matplotlib library. For example, a line chart showing the fluctuations in estrogen and progesterone is generated.

[1664] Finally, the server sends the generated graphs and charts to the user's device, where the user can view them and visually understand the hormonal fluctuations and accompanying physical changes during the next menstrual cycle.

[1665] This system allows users to accurately understand their own hormone fluctuations and manage their physical condition. In addition, the generative AI model can improve the accuracy of hormone fluctuation predictions by learning from user feedback.

[1666] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1667] Step 1:

[1668] The user provides input information through the terminal.

[1669] Specifically, users use a smartphone or computer to enter information about their menstrual cycle and changes in their physical condition into a dedicated application or web form.

[1670] An example of the data to be entered is as follows:

[1671] Menstrual cycle data: [28, 30, 27, 29]

[1672] Physical condition change data: ["headache", "fatigue", "depression"]

[1673] Input: Menstrual cycle data and physical condition change data entered by the user into the device

[1674] Output: Input data sent from the device to the server

[1675] Step 2:

[1676] The server receives the input and organizes the data.

[1677] The server receives the data submitted by the user and organizes it into a data frame using Python's Pandas library.

[1678] Specifically, the data is organized as follows:

[1679] Convert menstrual cycle data and physical condition change data into a data frame

[1680] Input: Menstrual cycle data and physical condition change data sent from the device

[1681] Output: A neat data frame

[1682] Step 3:

[1683] The server sends a prompt to the generative AI model.

[1684] The server generates prompt sentences based on the organized data and sends them to the generative AI model.

[1685] An example prompt is:

[1686] User's menstrual cycle data: [28, 30, 27, 29]

[1687] User's physical condition data: ["headache", "fatigue", "depression"]

[1688] Use this data to predict hormone fluctuations during your next menstrual cycle.

[1689] Input: A cleaned data frame

[1690] Output: The prompt sent to the generative AI model

[1691] Step 4:

[1692] Generative AI models predict hormone fluctuations.

[1693] A generative AI model (e.g., GPT-4) predicts hormone fluctuations based on the prompt text sent.

[1694] Specifically, it generates the following predictions:

[1695] The hormonal fluctuations during your next menstrual cycle are as follows:

[1696] Day 1: Estrogen levels drop

[1697] Day 5: Progesterone levels rise

[1698] Day 14: Estrogen levels peak

[1699] Day 21: Progesterone levels peak

[1700] Input: The prompt sent to the generative AI model

[1701] Output: The predicted results returned by the generative AI model

[1702] Step 5:

[1703] The server receives the prediction results and organizes the data.

[1704] The server receives the prediction results returned by the generative AI model and organizes them as a data frame again using Pandas.

[1705] Specifically, the data is organized as follows:

[1706] Convert the prediction results into a data frame

[1707] Input: Prediction results returned by the generative AI model

[1708] Output: A data frame of organized prediction results

[1709] Step 6:

[1710] The server visualizes the prediction results as graphs and charts.

[1711] The server visualizes the organized prediction results as graphs and charts using the Matplotlib library.

[1712] Specifically, the graph is generated as follows:

[1713] Create a line chart showing fluctuations in estrogen and progesterone

[1714] Input: Data frame of organized prediction results

[1715] Output: Generated graphs and charts

[1716] Step 7:

[1717] The server sends the visualization results to the user's terminal.

[1718] The server sends the generated graphs and charts to the user's terminal.

[1719] Specifically, the data may be sent using an HTTP response or through a dedicated application.

[1720] Input: Generated graphs and charts

[1721] Output: Visualization results sent to the user's device

[1722] Step 8:

[1723] The user checks the visualization results on the device.

[1724] Users can check the graphs and charts received on their device to visually understand the hormonal fluctuations and accompanying changes in physical condition during their next menstrual cycle.

[1725] Specifically, it displays graphs and charts through an application or web browser.

[1726] Input: Visualization results sent from the server

[1727] Output: Graphs and charts that users can view on their devices

[1728] (Application example 1)

[1729] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1730] Fluctuations in female hormones cause symptoms such as premenstrual syndrome (PMS) and have a significant impact on women's quality of life. However, these hormone fluctuations and the accompanying changes in physical condition are difficult for those around them to understand, and it is also difficult for women to recognize them themselves. Furthermore, there are challenges in receiving appropriate health management and product recommendations.

[1731] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1732] In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, a means for predicting hormone fluctuations based on information such as the user's menstrual cycle and changes in physical condition and visualizing the results, and a means for providing health care advice and product recommendations suited to the user. As a result, by accurately predicting and visualizing female hormone fluctuations, the user can more easily understand changes in their own physical condition and receive appropriate health care and product recommendations.

[1733] "Female hormone fluctuations" are changes in the levels of hormones such as estrogen and progesterone that fluctuate periodically in a woman's body.

[1734] "Visualization" refers to the display of data or information in a visual format such as a graph or chart.

[1735] "Generative AI" is an artificial intelligence technology that makes predictions and generates information based on input from users.

[1736] The "menstrual cycle" refers to the period from the first day of a woman's menstrual period to the first day of her next menstrual period.

[1737] "Changes in physical condition" refers to any physical or mental changes or symptoms experienced by the user.

[1738] "Health Management Advice" means specific suggestions or instructions to improve or maintain a user's health.

[1739] "Product suggestion" refers to recommending appropriate products based on the user's needs and condition.

[1740] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before their period.

[1741] "Feedback" refers to opinions and reactions provided by users, and is information used to improve the performance of a system.

[1742] The system for implementing this invention visualizes female hormone fluctuations and uses a generation AI to provide health management advice and product recommendations tailored to the user. Specific embodiments of this system are described below.

[1743] System configuration

[1744] The system consists of the following main components:

[1745] 1. User device: The user enters information about their menstrual cycle and changes in physical condition on a device such as a smartphone or tablet.

[1746] 2. Server: Runs the generative AI model, predicts hormone fluctuations based on user input, and visualizes the results.

[1747] 3. Generative AI model: Predicts hormone fluctuations based on user input and generates health management advice and product suggestions.

[1748] Program processing

[1749] 1. On the user's device, enter:

[1750] Users use a smartphone or tablet to enter information such as the start date of their menstrual cycle, the length of the cycle, and changes in their physical condition (e.g., fatigue, abdominal pain).

[1751] 2. Data processing on the server:

[1752] The server receives the data sent by the user and inputs it into a generative AI model, which predicts hormone fluctuations based on the user's menstrual cycle and changes in physical condition.

[1753] 3. Prediction and visualization of hormone fluctuations:

[1754] The generative AI model visualizes the predicted hormone fluctuations as graphs and charts, allowing users to visually understand their own hormone fluctuations.

[1755] 4. Health care advice and product suggestions:

[1756] The generative AI model uses predicted hormone fluctuations and information on changes in the user's physical condition to generate appropriate health management advice and product recommendations, such as suggesting supplements or fitness programs needed at specific times.

[1757] Hardware and software used

[1758] Hardware: smartphones, tablets, servers

[1759] Software: Generative AI models, data processing libraries (e.g., Python, Matplotlib)

[1760] Specific examples

[1761] The user uses their smartphone to enter the following information:

[1762] Menstrual cycle start date: October 1, 2023

[1763] Cycle length: 28 days

[1764] Changes in physical condition: fatigue, abdominal pain

[1765] Using this information, the generative AI model predicts hormone fluctuations over the next 28 days and generates prompts like this:

[1766] The user's menstrual cycle starts on October 1, 2023, and is 28 days long. The user is feeling tired and has abdominal pain. Based on this information, predict hormone fluctuations for the next 28 days and provide appropriate health management advice.

[1767] The generative AI model predicts hormone fluctuations based on the prompt text and visualizes them as graphs. It also provides users with appropriate health management advice and product recommendations, such as suggesting supplements or fitness programs needed at specific times.

[1768] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1769] Step 1:

[1770] The user uses a smartphone or tablet to input information such as the start date of the menstrual cycle, the length of the cycle, and changes in physical condition (e.g., fatigue, abdominal pain).

[1771] Input: menstrual cycle start date, cycle length, and information on changes in physical condition

[1772] Output: User input data

[1773] Specific operation: The user enters the required information into the application's input form and presses the submit button.

[1774] Step 2:

[1775] The terminal transmits the user's input data to the server.

[1776] Input: User-entered data

[1777] Output: User data sent to the server

[1778] Specific operation: The terminal sends the input data to the server as an HTTP request.

[1779] Step 3:

[1780] The server receives the data sent by the user and converts it into a data format for input into the generative AI model.

[1781] Input: User data sent to the server

[1782] Output: Data format that can be input to a generative AI model

[1783] Specific operation: The server parses the received data and converts it into the required format (e.g., JSON format).

[1784] Step 4:

[1785] The server uses the generated AI model to predict hormone fluctuations based on information about the user's menstrual cycle and changes in physical condition.

[1786] Input: Data format that can be input to the generative AI model

[1787] Output: Predicted hormone fluctuation data

[1788] Specific operation: The server inputs data into the generative AI model and obtains predicted results for hormone fluctuations.

[1789] Step 5:

[1790] The server visualizes the predicted hormone fluctuation data as graphs and charts.

[1791] Input: Predicted hormone fluctuation data

[1792] Output: Visualized graphs and charts

[1793] What it does: The server uses libraries such as Matplotlib to convert hormone fluctuation data into graphs and charts.

[1794] Step 6:

[1795] The server uses the generated AI model to generate health management advice and product suggestions appropriate for the user.

[1796] Input: Predicted hormone fluctuation data, user's physical condition change information

[1797] Output: Health care advice and product suggestions

[1798] Specific operation: The server inputs data into the generative AI model to generate health management advice and product suggestions.

[1799] Step 7:

[1800] The server transmits visualized graphs and charts, as well as health management advice and product suggestions, to the user's terminal.

[1801] Input: visualized graphs and charts, health management advice and product suggestions

[1802] Output: Data sent to the user's terminal

[1803] Specific operation: The server sends the generated data to the user terminal as an HTTP response.

[1804] Step 8:

[1805] The user terminal receives the data sent from the server and displays it on the application.

[1806] Input: Data sent from the server

[1807] Output: Graphs and charts displayed on the application, health management advice and product suggestions

[1808] Specific operation: The user device analyzes the received data and displays it on the application's UI.

[1809] Example 2

[1810] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1811] Fluctuations in female hormones cause symptoms such as PMS (premenstrual syndrome), placing a significant burden on women. However, these symptoms are often difficult to understand by those around them, and women themselves often find it difficult to recognize them as PMS. Conventional systems have low accuracy in predicting hormone fluctuations based on individual users' physical changes and menstrual cycles, making it difficult to take appropriate measures. Therefore, there is a need for a system that can accurately predict hormone fluctuations based on a user's physical changes and menstrual cycles, and visualize the results.

[1812] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1813] In this invention, the server includes means for the user to input data regarding changes in physical condition and menstrual cycle, means for transmitting the data to the server, means for storing the data, means for providing the data to a generative AI model, means for the generative AI model to predict hormone fluctuations based on the data and visualize the results, and means for the generative AI model to learn from feedback from the user and improve the accuracy of hormone fluctuation predictions.This makes it possible to solve the problem that women who suffer from PMS have difficulty getting understanding from those around them and also have difficulty recognizing that they are suffering from PMS themselves.

[1814] "User" refers to an individual who uses the system to input data regarding changes in physical condition and menstrual cycle.

[1815] "Changes in physical condition" refers to fluctuations in the user's physical or mental state, and specifically includes the start date of menstruation, body temperature, mood changes, etc.

[1816] "Menstrual cycle" refers to a woman's menstrual cycle and includes the period from the first day of menstruation to the first day of the next menstruation.

[1817] "Data" refers to information entered by the user regarding changes in physical condition and menstrual cycle.

[1818] "Terminal" refers to a device used by a user to input data, and specifically includes smartphones and personal computers.

[1819] "Server" refers to the computer system that receives, stores, and provides user-submitted data to the generative AI model.

[1820] A "generative AI model" refers to an artificial intelligence model that uses machine learning algorithms to learn from user data and predict hormone fluctuations.

[1821] "Hormonal fluctuations" refers to fluctuations in hormone levels that occur in a woman's body, particularly those associated with the menstrual cycle.

[1822] "Prediction" refers to the generative AI model estimating future hormone fluctuations based on user data.

[1823] "Visualization" refers to displaying the results of hormone fluctuations predicted by the generative AI model in a way that is easy for users to understand.

[1824] "Feedback" refers to additional data or opinions that users provide to the system, which are used to train the generative AI model.

[1825] "PMS (premenstrual syndrome)" refers to the physical and mental symptoms women experience before their period, including abdominal pain, headaches, and mood swings.

[1826] This invention is a system in which a user inputs data on changes in physical condition and menstrual cycle, and a generative AI model predicts hormone fluctuations based on that data and visualizes the prediction results. Specific embodiments of this system are described below.

[1827] Hardware and Software Configuration

[1828] Terminal

[1829] Users enter data using devices such as smartphones or computers. A dedicated application or website is installed on the device, and users enter data on changes in their physical condition and menstrual cycle through this.

[1830] server

[1831] The server receives and stores data sent by users. The server has a database and organizes and stores data for each user. The server also provides data to the generative AI model.

[1832] Generative AI Models

[1833] The generative AI model predicts hormone fluctuations based on data provided by the server, and uses machine learning algorithms to learn from user feedback and improve prediction accuracy.

[1834] Data processing and calculation

[1835] Entering and Submitting Data

[1836] The user enters data about their physical condition and menstrual cycle through the device. For example, the user might enter "My period started on October 1, 2023." The device then encrypts this data and sends it to the server using the HTTPS protocol.

[1837] Data storage

[1838] The server stores the received data in a database. For example, it stores it in the format "User ID: 12345, Menstruation Start Date: October 1, 2023".

[1839] Data provision

[1840] The server retrieves the data for "User ID: 12345" from the database and provides it to the generative AI model.

[1841] Model Updates and Predictions

[1842] The generative AI model updates the neural network based on the provided data and adjusts the parameters for predicting the start date of the next period. For example, the generative AI model learns from "User ID: 12345's menstrual start data on October 1, 2023" and more accurately predicts the start date of the next period.

[1843] Providing prediction results

[1844] The generative AI model generates a prediction result and returns it to the server in the format "User ID: 12345, Next Menstrual Start Date: October 29, 2023." The server then sends this result to the device, where the user can check it in the "Prediction Results" section of the app.

[1845] Specific examples

[1846] For example, if a user types, "My period started on October 1, 2023. Please predict when my next period will start," the server receives this information and provides it to the generative AI model, which then learns from this new data and more accurately predicts when her next period will start.

[1847] This system allows users to more accurately understand changes in their physical condition and take appropriate measures.

[1848] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1849] Step 1:

[1850] Users input data about changes in their physical condition and menstrual cycle. Specifically, they use a dedicated application or website on their smartphone or computer. For example, a user might enter, "My period started on October 1, 2023." The input data is temporarily stored on the device.

[1851] Step 2:

[1852] The device sends the data entered by the user to the server. The data is encrypted and securely sent to the server. For example, the device encrypts the data "My period started on October 1, 2023" and sends it to the server using the HTTPS protocol. The input is the user's physical condition data, and the output is encrypted data.

[1853] Step 3:

[1854] The server stores the received data in a database. The stored data is organized by user and can be accessed later by the generative AI model. For example, the server stores the data in the database in the format "User ID: 12345, Menstruation Start Date: October 1, 2023". The input is encrypted data, and the output is the data in the format stored in the database.

[1855] Step 4:

[1856] The server provides the stored data to the generative AI model, which then updates the hormone fluctuation prediction model based on this data. For example, the server provides the generative AI model with "User ID: 12345's menstrual start data on October 1, 2023." The input is the data retrieved from the database, and the output is the data provided to the generative AI model.

[1857] Step 5:

[1858] The generative AI model learns from the provided data and updates the hormone fluctuation prediction model. Specifically, it uses a machine learning algorithm to learn new data and improve prediction accuracy. For example, the generative AI model learns from "User ID: 12345's menstrual start data on October 1, 2023" and more accurately predicts the start date of the next menstrual period. The input is the provided data, and the output is an updated prediction model.

[1859] Step 6:

[1860] The generative AI model provides prediction results to the user based on the updated prediction model. The user can check the prediction results through their device. For example, the generative AI model predicts that "User ID: 12345's next menstrual start date is October 29, 2023" and returns this result to the server. The server sends this result to the device, and the user can check it in the "Prediction Results" section of the app. The input is the updated prediction model, and the output is the prediction results provided to the user.

[1861] (Application example 2)

[1862] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1863] Until now, there has been no system that can accurately predict fluctuations in female hormones and the accompanying changes in physical condition, and provide appropriate services based on that prediction. As a result, when women suffer from premenstrual syndrome (PMS), it is difficult for those around them to understand, and it is also difficult for them to recognize it themselves. There has also been a lack of means to provide appropriate electronic payment services in response to changes in physical condition.

[1864] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for visualizing female hormone fluctuations, a means for using a generation AI, and a means for the generation AI to predict hormone fluctuations based on data on the user's physical condition changes and menstrual cycle, and for providing electronic payment services based on the prediction results. This makes it possible to provide optimal electronic payment services in response to changes in the user's physical condition, making it easier for women suffering from PMS to gain understanding from those around them and to recognize the symptoms of PMS themselves.

[1865] A "means for visualizing female hormone fluctuations" is a device or software for visually displaying fluctuations in a user's hormone levels.

[1866] "Means using generative AI" refers to devices or software that use artificial intelligence techniques to analyze data and generate predictive models.

[1867] "Data on changes in the user's physical condition and menstrual cycle" refers to information entered by the user regarding changes in physical condition and menstrual cycle.

[1868] A "means for predicting hormone fluctuations" is a device or software that predicts future fluctuations in hormone levels based on data on changes in the user's physical condition and menstrual cycle.

[1869] "Means for providing electronic payment services" refers to devices or software for providing services related to electronic payments, such as cashback or discount coupons, based on the results of predictions of the user's physical condition and hormone fluctuations.

[1870] "PMS (premenstrual syndrome)" is a general term for the physical and psychological symptoms that women experience before their period.

[1871] A system for implementing the present invention has the following configuration.

[1872] System configuration

[1873] 1. Hardware

[1874] Server: Stores data and runs generative AI models.

[1875] Terminal: The device (smartphone, tablet, etc.) where users enter data and view results.

[1876] 2. Software

[1877] Generative AI model: A machine learning model that predicts hormone fluctuations based on data on the user's physical condition and menstrual cycle.

[1878] Database: A system for storing user physical condition data and menstrual cycle data.

[1879] Electronic payment service: An application that provides cashback and discount coupons based on the user's physical c...

Claims

1. A means for a user to provide input information through a terminal; A server receives the input information and organizes the data; A means for the server to send a prompt sentence regarding the input information to a generative AI model; A means for the generative AI model to predict hormone fluctuations based on the input information; A means for the server to receive data relating to prediction of hormone fluctuations and organize the data relating to prediction of hormone fluctuations; A means for visualizing the data related to the prediction of hormone fluctuations organized by the organizing means in the form of a graph or chart, a means for transmitting a visualization result visualized by the visualization means to the terminal of the user by the server; A means for the user to confirm the visualization result on the terminal; A system including:

2. The system of claim 1, wherein the generative AI model predicts hormonal fluctuations based on information such as the user's menstrual cycle and changes in physical condition, and visualizes the results.

3. The system of claim 1 , wherein the generative AI model learns from feedback from users to improve accuracy in predicting hormone fluctuations.

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