System

Abstract

To provide a system.SOLUTION: A system includes: means for receiving input of data of men and women who have children and obtaining an optimal solution of a timed intercourse; means for unnecessitating hospital visits for timed intercourse by notifying at least either a man or a woman of the optimal solution of the timed intercourse; means for determining a gene that increases an implantation rate of at least either external fertilization or microinsemination in gene information of either a man or a woman; and means for generating a prompt statement inputted to a generative AI model on the basis of a determination result, inputting the generated prompt statement to the generative AI model, acquiring an optimal therapy from the generative AI model and providing it.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] Japan's total population is on the decline, and there are households that want to have children but cannot. In addition, current timing therapy requires outpatient visits, which is difficult for dual-income households to coordinate. [Means for solving the problem]

[0005] To solve this problem, we provide a system that inputs data on men and women to find the optimal solution for timing therapy. We also provide a system that includes a method to eliminate the need for outpatient visits and a method to identify genes that increase implantation rates in IVF and ICSI. This will reduce the burden of infertility treatment and help curb population decline. [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 form of the present invention is a system that registers information such as age, health status, and lifestyle habits into a database as a means of inputting data on men and women. Specifically, users enter their own information through a dedicated interface, and the system collects and analyzes it.

[0029] "Example 2"

[0030] Additionally, as a means of finding the optimal solution for timing therapy, an algorithm will be implemented to calculate the optimal timing based on the collected data. Specifically, the system will predict the day of ovulation based on information such as a woman's menstrual cycle and body temperature fluctuations, and notify the user of the optimal timing based on that.

[0031] "Example 3"

[0032] Furthermore, gene sequencing technology is used as a means of identifying genes that increase implantation rates in IVF and ICSI. Specifically, the genetic information provided by the user is analyzed to identify genes that have the potential to increase implantation rates. This makes it possible to propose the most suitable treatment for each user.

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

[0034] "Example 1"

[0035] Step 1: The user enters information such as their age, health condition, and lifestyle habits through a dedicated interface.

[0036] Step 2: The system registers the information provided by the user in a database.

[0037] Step 3: The system analyzes the registered information and suggests the most appropriate treatment for the user.

[0038] "Example 2"

[0039] Step 1: The system uses information provided by the user to predict the date of ovulation based on the woman's menstrual cycle, body temperature fluctuations, etc.

[0040] Step 2: The system calculates the optimal timing based on the predicted ovulation date.

[0041] Step 3: The system notifies the user of the optimal timing.

[0042] "Example 3"

[0043] Step 1: The system collects genetic information provided by the user.

[0044] Step 2: The genetic information collected by the system is analyzed using gene sequencing technology.

[0045] Step 3: The system uses the analysis results to identify genes that may increase implantation rates and suggests the most appropriate treatment for the user.

[0046] Example 1

[0047] 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."

[0048] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit the hospital, placing a significant time and financial burden on them. Genetic identification to increase implantation rates is also difficult, making it difficult to find the optimal treatment. Furthermore, there is a lack of efficient means to collect and analyze information such as the patient's age, health condition, and lifestyle, making personalized treatment difficult.

[0049] 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.

[0050] In this invention, the server includes: a means for the user to input information such as age, health condition, and lifestyle habits through a dedicated interface; a means for the terminal to send the input data to the server; a means for the server to register the data in a database; a means for the server to collect and analyze the data; a means for the server to input the analysis results into a generative AI model; and a means for the server to return the results of the generative AI model to the user. This allows patients to input their own information and find the optimal treatment without having to visit a hospital. It also enables efficient genetic identification and the provision of personalized treatments.

[0051] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[0052] "Timing therapy" is a therapy that maximizes the therapeutic effect by administering treatment at a specific timing.

[0053] "Measures that eliminate the need for hospital visits" are measures that allow patients to receive treatment or diagnosis remotely without having to visit a medical institution in person.

[0054] "In vitro fertilization / microinsemination" is an assisted reproductive technique in which sperm and eggs are fertilized outside the body, and the fertilized eggs are then transplanted into the uterus.

[0055] The "means for identifying genes that increase implantation rates" refers to means for identifying specific genes that increase implantation rates using gene sequencing technology.

[0056] A "dedicated interface" is a specific input screen or application designed for users to enter their information.

[0057] A "terminal" is a device (e.g., a smartphone or PC) that a user uses to input information and send it to a server.

[0058] "Server" means a computer system that receives, processes, stores, and analyzes data sent by users.

[0059] A "database" is a system for efficiently storing and managing collected data.

[0060] A "generative AI model" is an artificial intelligence model that generates analytical results based on input data.

[0061] A "prompt" is an instruction to be input into a generative AI model, and is a statement used to perform a specific analysis or generation.

[0062] This invention is a system that allows users to input information such as age, health status, and lifestyle habits through a dedicated interface, registers the information in a database, and collects and analyzes it.The system then inputs the analysis results into a generative AI model, and returns the generated results to the user, providing optimal treatment.

[0063] Users enter their information using a dedicated interface (e.g., a web application or a mobile application). This interface is built using web technologies such as HTML, CSS, and JavaScript (registered trademark). The information entered by users includes age, health status, lifestyle habits, etc.

[0064] The terminal (user's device) sends the entered data to the server as an HTTP request. At this time, the data is sent in JSON format. The server analyzes the received data and registers it in a database. The server receives and registers the data using frameworks such as Python's Flask or Django, or Node.js's Express. MySQL (registered trademark) or PostgreSQL is used as the database.

[0065] After the data is registered in the database, the server collects and analyzes the data using data analysis libraries such as Python's pandas and scikit-learn. For example, it can analyze trends in the age distribution and health status of users.

[0066] The server inputs the analysis results into a generative AI model. For example, OpenAI's GPT-3 (registered trademark) is used as the generative AI model. A prompt sentence is generated and input into the model to obtain detailed analysis results. Examples of prompt sentences include the following:

[0067] Prompt: "If a 30-year-old user exercises daily and is in good health, analyze what health trends they have compared to other users of the same age."

[0068] The server returns the results obtained from the generative AI model to the user. The results are displayed on the user's device. For example, HTML and JavaScript are used to display the results on a web page. The user can view the results through the web page.

[0069] As a concrete example, consider a scenario in which a user uses a dedicated web interface to enter the following information:

[0070] Age: 30

[0071] Health: Good

[0072] Lifestyle: Exercise every day

[0073] This information is sent to a server and registered in a database. The registered data is collected by the server and used to analyze age distribution and health trends. The analysis results are input into a generative AI model, which generates detailed results. For example, a result may be that "30-year-old users who exercise daily have a high rate of good health compared to their peers." The results are displayed on the user's device, where they can check them.

[0074] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0075] Step 1:

[0076] The user enters information

[0077] Using a dedicated interface, users enter information such as their age, health status, and lifestyle habits. Specifically, they open a web browser, access a dedicated web page, enter the required information in a form, and click the "Submit" button. The input data includes age (e.g., 30 years old), health status (e.g., good), and lifestyle habits (e.g., exercise daily).

[0078] Step 2:

[0079] The device sends the input data to the server

[0080] The terminal converts the data entered by the user into JSON format and sends it to the server as an HTTP POST request. Specifically, it sends the following JSON data:

[0081] json{"age": 30,"health_status": "good","lifestyle": "exercise daily"}

[0082] The input is the data entered by the user, and the output is the JSON data sent to the server.

[0083] Step 3:

[0084] The server registers the data in the database

[0085] The server parses the received JSON data and registers it in a database. Specifically, it uses frameworks such as Python's Flask or Django, or Node.js's Express to receive and register the data. MySQL or PostgreSQL is used as the database. The following SQL query is executed.

[0086] sql

[0087] INSERT INTO user_data (age, health_status, lifestyle) VALUES (30, 'Good', 'Exercise daily');

[0088] The input is the JSON data sent to the server, and the output is the data registered in the database.

[0089] Step 4:

[0090] The server collects and analyzes the data

[0091] The server retrieves all user data from the database and analyzes trends in age distribution and health status. Specifically, it uses data analysis libraries such as Python's pandas and scikit-learn. The following Python code is used:

[0092] python import pandas as pd

[0093] Retrieving data from a database

[0094] data = pd.read_sql('SELECT FROM user_data', connection)

[0095] Age distribution analysis

[0096] age_distribution = data['age'].value_counts()

[0097] Analyze health trends

[0098] health_trends = data['health_status'].value_counts()

[0099] The input is all user data obtained from the database, and the output is the analysis results of age distribution and health status trends.

[0100] Step 5:

[0101] The server inputs the analysis results into the generative AI model

[0102] The server generates a prompt based on the analysis results and inputs it into the generative AI model. Specifically, it generates the following prompt:

[0103] Prompt: "If a 30-year-old user exercises daily and is in good health, analyze what health trends they have compared to other users of the same age."

[0104] The input is the analysis results of age distribution and health status trends, and the output is a prompt sentence to be input into the generative AI model.

[0105] Step 6:

[0106] The server returns the results of the generated AI model to the user

[0107] The server returns the results obtained from the generative AI model to the user. Specifically, it uses HTML and JavaScript to display the results obtained from the generative AI model on the user's web page. For example, it displays the results as follows:

[0108] The result: "30-year-old users who exercise daily are more likely to be in good health than their peers."

[0109] The input is the result obtained from the generative AI model, and the output is the result displayed on the user's device.

[0110] (Application example 1)

[0111] 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."

[0112] Conventional timing therapy, IVF, and ICSI require patients to visit the hospital, which places a significant burden on them. Furthermore, optimal dietary plans based on individual health conditions and lifestyle habits were not proposed, and health management was not adequately implemented. This resulted in the problem of not maximizing the effectiveness of treatment.

[0113] 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.

[0114] In this invention, the server includes a means for inputting male and female data and obtaining an optimal solution for timing therapy, a means for eliminating the need for outpatient visits for timing therapy, a means for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc., and a means for collecting information such as the user's age, health condition, and lifestyle habits and proposing an individually optimized diet plan. This reduces the burden of outpatient visits for patients and makes it possible to maximize the effectiveness of treatment by proposing an optimal diet plan based on the individual's health condition and lifestyle habits.

[0115] "Gender data" refers to information about individuals such as age, gender, health status, and lifestyle habits.

[0116] "Timing therapy" refers to a medical technique that maximizes effectiveness by administering treatment at a specific time.

[0117] An "optimal solution" refers to the most effective solution given the conditions and constraints.

[0118] "Non-visit measures" refer to methods that allow patients to receive treatment or diagnosis without visiting a medical institution in person.

[0119] "In vitro fertilization / microinjection" refers to an assisted reproductive technique in which an egg and sperm are fertilized outside the body and the fertilized egg is then returned to the uterus.

[0120] "Genes that increase implantation rate" refer to genes that increase the probability of a fertilized egg implanting in the uterine lining.

[0121] "Genetic discrimination means" refers to a method for identifying a specific gene and confirming its presence or characteristics.

[0122] "Information on age, health status, lifestyle habits, etc." refers to data such as an individual's age, current health status, and daily lifestyle habits.

[0123] "Individually optimized meal plans" refer to customized meal suggestions based on an individual's health and lifestyle.

[0124] "Means of collection and analysis" refers to the methods of collecting data and analyzing it to derive useful information.

[0125] A system for implementing this invention includes a server, a user terminal, and a database. The server runs a program for collecting and analyzing data from users. The user terminal is a device such as a smartphone or PC, and provides an interface for users to input their information. The database stores the collected data and allows the server to access it as needed.

[0126] The server executes a program that includes the following means:

[0127] 1. Data collection methods:

[0128] The system collects information such as age, health status, and lifestyle habits sent from the user's device. The user inputs this information through a dedicated interface. For example, the user inputs their age and health status using a smartphone app.

[0129] 2. Data analysis methods:

[0130] The server analyzes the collected data and generates an individually optimized meal plan using a programming language such as Python. For example, if the user has diabetes, it will suggest a low-carb menu.

[0131] 3. Proposal method:

[0132] Based on the analysis results, the app will suggest the optimal meal plan for the user. The suggestion will be sent to the user's device. For example, when the user opens the app, the recommended meal plan will be displayed.

[0133] The specific hardware and software configuration is as follows:

[0134] Hardware: smartphones, PCs, servers

[0135] Software: Python, database management system (e.g., MySQL), user interface library (e.g., Tkinter)

[0136] The server stores the data entered by the user in a database in JSON format and analyzes it as needed. The analysis results are sent to the user's device, and the user can receive an optimal meal plan based on their health condition and lifestyle.

[0137] As a concrete example, the following prompt sentence can be input to a generative AI model:

[0138] Example prompt sentence:

[0139] Develop an application that suggests optimal meal plans based on the user's age, health condition, and lifestyle habits. Information entered by the user includes name, age, health condition (e.g., diabetes, high blood pressure, etc.), and lifestyle habits (e.g., lack of exercise, smoking, etc.). Implement a function that collects and analyzes this information to suggest individually optimized menus.

[0140] In this way, users can receive optimal meal plans based on their own health condition and lifestyle habits, enabling them to effectively manage their health.

[0141] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0142] Step 1:

[0143] The user accesses a dedicated interface using a terminal and inputs information such as their age, health condition, and lifestyle habits. The input data is then sent from the terminal to the server. Examples of input data include "age: 35, health condition: diabetes, lifestyle habits: lack of exercise."

[0144] Step 2:

[0145] The server receives the user data sent from the terminal and stores it in a database. It uses a database management system (e.g., MySQL) to store the data in an appropriate format. The input data is stored in JSON format.

[0146] Step 3:

[0147] The server runs programs to analyze the stored user data. It uses programming languages ​​such as Python to analyze the data based on the user's age, health status, and lifestyle. For example, an algorithm might be run to suggest low-carb meals to a diabetic user.

[0148] Step 4:

[0149] The server generates an individually optimized meal plan based on the results of the data analysis. The meal plan is best suited to the user's health condition and lifestyle. For example, it might suggest a menu such as "Breakfast: low-carb bread, Lunch: salad chicken, Dinner: grilled fish."

[0150] Step 5:

[0151] The server sends the generated meal plan to the user's terminal, where the user can check the proposed meal plan through the terminal interface, and a notification function is used to notify the user that a new meal plan has been proposed.

[0152] Step 6:

[0153] The user can review the proposed meal plan and provide feedback if necessary. This feedback will be reflected in the next data analysis, leading to more accurate suggestions. For example, users can provide feedback such as, "I don't like this menu."

[0154] In this way, users can receive optimal meal plans based on their own health condition and lifestyle habits, enabling them to effectively manage their health.

[0155] Example 2

[0156] 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."

[0157] Conventional timing therapy requires clinic visits, which places a significant burden on users in terms of time and money. Furthermore, if the ovulation date is not accurately predicted, the effectiveness of timing therapy decreases. Furthermore, it is difficult to distinguish genes to improve the implantation rate in in vitro fertilization and intracytoplasmic sperm injection. To solve these problems, a more accurate and efficient timing therapy system is needed.

[0158] 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.

[0159] In this invention, the server includes a means for inputting male and female data and obtaining an optimal solution for timing therapy, a means for eliminating the need for outpatient visits for timing therapy, a means for identifying genes that increase the implantation rate of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICS), a means for collecting and preprocessing data on menstrual cycles and body temperature fluctuations to predict the day of ovulation, and a means for notifying the user of the prediction results. This allows users to accurately predict the day of ovulation and receive optimal timing therapy without visiting a hospital. Furthermore, genetic identification can improve the implantation rate of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICS).

[0160] "Data for men and women" includes information such as age, health status, and lifestyle habits.

[0161] The "optimal solution for timing therapy" refers to the optimal timing calculated based on data such as menstrual cycle and body temperature fluctuations.

[0162] The "means for eliminating the need for hospital visits for timing therapy" refers to a means that allows the user to perform timing therapy without visiting a medical institution.

[0163] "Methods for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc." refers to the use of gene sequencing technology to identify genes that improve implantation rates.

[0164] "Data on menstrual cycle and body temperature fluctuations" refers to data that includes information such as the start date of a woman's menstruation and her basal body temperature.

[0165] "Preprocessing" refers to the process of cleaning the collected data, such as filling in missing values ​​and removing outliers.

[0166] The "means for predicting the date of ovulation" is a means for predicting the date of ovulation based on preprocessed data using a machine learning model or the like.

[0167] "Means for notifying the user of the prediction results" refers to sending information such as the predicted ovulation date to the user's device via push notification or email.

[0168] This invention is a system for obtaining an optimal solution for timing therapy, which inputs data on men and women, collects data on menstrual cycles and body temperature fluctuations, and implements an algorithm to predict the day of ovulation. A specific embodiment of this system will be described below.

[0169] System configuration

[0170] server:

[0171] The server plays a central role in collecting data, preprocessing, predicting ovulation days, and notifying results. Specifically, the following software and hardware are used:

[0172] Database: A relational database such as MySQL or PostgreSQL

[0173] Data preprocessing: Python's Pandas library

[0174] Machine learning models: Scikit-learn and TENSORFLOW (registered trademark)

[0175] Notification systems: Firebase Cloud Messaging and SendGrid

[0176] Device:

[0177] A terminal is a device where a user inputs data and receives notifications from a server. Specifically, this applies to smartphones and PCs. Data is input and notifications are received using dedicated mobile or web applications.

[0178] User:

[0179] The user is responsible for inputting data on their menstrual cycle and body temperature fluctuations. Using a dedicated app, the user inputs data such as the start date of their menstrual period and basal body temperature.

[0180] System Operation

[0181] 1. Data Entry:

[0182] Users enter data about their menstrual cycle and body temperature fluctuations into a dedicated app. For example, a user opens the app on their smartphone and enters the start date of their period and their basal body temperature.

[0183] 2. Data Collection:

[0184] The device sends the data entered by the user to the server, and the app sends the data to the server via the Internet, and the server stores the received data in a database.

[0185] 3. Data Preprocessing:

[0186] The server preprocesses the collected data, specifically by imputing missing values ​​and removing outliers, and cleans the data using Python's Pandas library to standardize the preprocessed data.

[0187] 4. Ovulation Prediction:

[0188] The server runs an algorithm to predict ovulation based on the pre-processed data, using a machine learning model to predict ovulation and calculate when the next ovulation will occur.

[0189] 5. Result notification:

[0190] The server notifies the user of the calculated optimal timing by sending a push notification or email using Firebase Cloud Messaging or SendGrid, and the device receives the notification from the server and displays it to the user.

[0191] Specific examples

[0192] User A enters data on her menstrual cycle and body temperature fluctuations into a dedicated app. For example, User A opens the app and enters "May 1, 2023" as the start date of her period and "36.5°C" as her basal body temperature. The device sends this data to the server. The server stores the received data in a database, complements missing values, and removes outliers. Next, the server runs an algorithm to predict the date of ovulation based on the preprocessed data, calculating that the next ovulation date will be "May 15, 2023." Finally, the server sends this information to User A's smartphone via push notification, and the device displays the message, "The next ovulation date is May 15, 2023."

[0193] Prompt Sentence Examples

[0194] "Please predict the next ovulation date based on the user's menstrual cycle and temperature fluctuation data. Also generate a message to notify the user of the prediction result."

[0195] In this way, specific embodiments of the system are described to clarify the details of the invention.

[0196] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0197] Step 1:

[0198] Data Entry

[0199] Users enter data about their menstrual cycle and body temperature fluctuations into a dedicated app. Specifically, users open the app on their smartphone and enter the start date of their period and their basal body temperature.

[0200] Input: menstruation start date, basal body temperature

[0201] Output: Input data is saved to the device

[0202] Step 2:

[0203] Data collection

[0204] The device sends the data entered by the user to the server, and the app sends the data to the server via the Internet, and the server stores the received data in a database.

[0205] Input: User-entered menstruation start date and basal body temperature

[0206] Output: Data stored on the server

[0207] Step 3:

[0208] Data Preprocessing

[0209] The server preprocesses the collected data, specifically by imputing missing values ​​and removing outliers, and cleans the data using Python's Pandas library to standardize the preprocessed data.

[0210] Input: Data stored on the server

[0211] Output: Preprocessed data

[0212] Step 4:

[0213] Ovulation prediction

[0214] The server runs an algorithm to predict ovulation based on the pre-processed data, using a machine learning model to predict ovulation and calculate when the next ovulation will occur.

[0215] Input: Preprocessed data

[0216] Output: Predicted ovulation date

[0217] Step 5:

[0218] Result notification

[0219] The server notifies the user of the calculated optimal timing by sending a push notification or email using Firebase Cloud Messaging or SendGrid, and the device receives the notification from the server and displays it to the user.

[0220] Input: Predicted ovulation date

[0221] Output: Notification displayed on the user's device

[0222] In this way, each processing step is specifically explained and the operation of the system is made clear.

[0223] (Application example 2)

[0224] 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."

[0225] Conventional timing therapy and in vitro fertilization / microinsemination require patients to visit a hospital, which places a significant burden on the user in terms of time and money. It is also difficult to accurately predict the day of ovulation, making it difficult to find the optimal timing. Furthermore, health data is recorded and analyzed manually, which makes it difficult to provide real-time notifications.

[0226] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for hospital visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization, intracytoplasmic sperm injection, etc., means for predicting the date of ovulation from information such as the menstrual cycle and body temperature fluctuations and notifying the user of the optimal timing, and means for recording and analyzing the user's health data using a smartphone or smart glasses and notifying the user in real time. This allows the user to receive an accurate prediction of the date of ovulation and notification of the optimal timing without having to visit a hospital.

[0227] "Data on men and women" refers to data including information on age, health status, lifestyle habits, etc.

[0228] The "optimal solution for timing therapy" is a solution that calculates the optimal ovulation date and timing of fertilization based on information such as the menstrual cycle and body temperature fluctuations.

[0229] The "means for eliminating the need for hospital visits for timing therapy" refers to a means that allows a user to perform timing therapy without visiting a medical institution.

[0230] "Methods for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc." refers to the use of gene sequencing technology to identify specific genes that improve implantation rates.

[0231] "A means for predicting the date of ovulation from information such as menstrual cycle and body temperature fluctuations, and notifying the user of the optimal timing" is a means for analyzing the user's menstrual cycle and body temperature data, predicting the date of ovulation, and notifying the user of that information.

[0232] "Means for recording and analyzing a user's health data using a smartphone or smart glasses and notifying them in real time" refers to means for using a smartphone or smart glasses to collect and analyze a user's health data and notify the user of the results in real time.

[0233] A system for implementing the present invention is configured using hardware such as a server, a smartphone, smart glasses, etc. Specific embodiments are described below.

[0234] The server inputs data on men and women and provides a means to find the optimal timing therapy solution. This data includes information on age, health status, lifestyle habits, etc. The server analyzes this data and implements an algorithm that predicts the day of ovulation based on information such as menstrual cycle and temperature fluctuations. This algorithm is implemented using a programming language such as Python.

[0235] Users use their smartphones or smart glasses to record their health data, including the start date of their menstrual period and body temperature. This data is sent in real time to a server, which analyzes the data and calculates the optimal timing. The calculated results are then sent to the user via their smartphone or smart glasses.

[0236] As a specific example, consider a scenario in which a user uses a smartphone app to input the start date of their menstrual period, and the app predicts and notifies them of the next ovulation date. For example, if the user inputs the start dates of their menstrual period as September 1, 2023, September 29, 2023, and October 27, 2023, the app will predict the next ovulation date as November 10, 2023 and notify them.

[0237] Here's an example prompt that uses a generative AI model to analyze user data and calculate the optimal timing:

[0238] "When a user enters the first date of their period, the app will predict and notify them of their next ovulation date. For example, if a user enters the first date of their period on September 1, 2023, September 29, 2023, or October 27, 2023, the app will predict and notify them of their next ovulation date as November 10, 2023."

[0239] In this way, users can receive accurate predictions of their ovulation date and notifications of the optimal timing without having to visit a doctor.

[0240] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0241] Step 1:

[0242] The user uses a smartphone or smart glasses to input data such as the start date of their menstrual period and their body temperature. The input data is collected by the smartphone or smart glasses application. The input data includes the start date of their menstrual period (e.g., September 1, 2023, September 29, 2023, October 27, 2023) and body temperature data. This data serves as the basis for understanding the user's health condition.

[0243] Step 2:

[0244] The device (smartphone or smart glasses) sends the collected data to a server. The data includes the user's menstrual start date and body temperature. The server receives this data and stores it in a database. The data is sent in real time via the Internet.

[0245] Step 3:

[0246] The server analyzes the received data. Specifically, it calculates the average length of the menstrual cycle and predicts the next ovulation date. For example, if the menstrual start dates are September 1, 2023, September 29, 2023, and October 27, 2023, the server calculates the interval between these dates to determine the average cycle. It then subtracts 14 days from the average cycle and predicts the next ovulation date.

[0247] Step 4:

[0248] The server calculates the optimal timing based on the predicted ovulation date. The calculated timing is prepared as data to be notified to the user. For example, if the next ovulation date is predicted to be November 10, 2023, the server generates a message to notify the user of this date.

[0249] Step 5:

[0250] The server sends the generated notification message to the device. The notification message includes information about the predicted ovulation date and the optimal timing. The device displays the received notification message to the user. The user can receive the notification in real time via their smartphone or smart glasses.

[0251] Step 6:

[0252] The user can take appropriate action based on the information provided. For example, the information can be used to help manage the user's health, such as timing therapy to coincide with the predicted ovulation date. This makes it possible to receive accurate ovulation date predictions and notifications of the optimal timing without having to visit a doctor.

[0253] Example 3

[0254] 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."

[0255] Conventional infertility treatments include timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI), but it has been difficult to find the optimal timing for these treatments and to identify genes that increase implantation rates. Furthermore, the need for outpatient visits places a significant burden on patients in terms of time and money. Furthermore, there has been a lack of optimal treatment proposals based on the results of genetic analysis, which often results in treatment effectiveness not being maximized.

[0256] 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.

[0257] In this invention, the server includes a means for a user to upload genetic information, a means for the server to receive and store the genetic information, a means for the server to analyze the genetic information, a means for the server to generate a prompt sentence based on the analysis results, a means for the server to send the prompt sentence to the generative AI model, a means for the generative AI model to generate a proposal, and a means for the server to provide the proposal to the user. This allows a user to upload genetic information from home, and the server analyzes the information and proposes the optimal treatment. This eliminates the need for hospital visits, reduces the time and financial burden, and maximizes the effectiveness of treatment.

[0258] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[0259] "Timing therapy" is a treatment that involves having sexual intercourse at the optimal time to increase the chances of pregnancy.

[0260] "In vitro fertilization" is an infertility treatment in which eggs and sperm are fertilized outside the woman's body, and the fertilized eggs are then returned to the uterus.

[0261] "Intracytoplasmic sperm injection" is an infertility treatment method that uses a microscope to inject sperm directly into an egg to achieve fertilization.

[0262] "Implantation rate" refers to the success rate of a fertilized egg implanting in the uterine lining.

[0263] "Gene sequencing technology" is a technology for analyzing the base sequence of DNA.

[0264] The "means for users to upload genetic information" refers to an interface that allows users to provide their own genetic information to the system.

[0265] "Means for the server to receive and store genetic information" refers to the function by which the server receives genetic information provided by the user and safely stores it.

[0266] The "means by which the server analyzes genetic information" refers to a combination of software and hardware that allows the server to analyze the stored genetic information.

[0267] "Means for the server to generate a prompt sentence based on the analysis results" refers to the function by which the server creates a prompt sentence to be input into the generative AI model based on the analysis results of genetic information.

[0268] "Means for the server to send a prompt sentence to the generative AI model" refers to a communication means for the server to send the generated prompt sentence to the generative AI model.

[0269] "Means by which the generative AI model generates suggestions" refers to the function by which the generative AI model suggests the optimal treatment for the user based on a prompt sentence.

[0270] The "means by which the server provides suggestions to the user" is an interface by which the server notifies and provides suggestions from the generative AI model to the user.

[0271] This invention is a system that analyzes genetic information provided by a user, identifies genes that have the potential to increase implantation rates, and proposes optimal treatment methods. Specific embodiments of this system are described below.

[0272] First, users upload their genetic information through a dedicated web interface. This web interface is designed so that users can simply select a file and click the upload button. Once the user uploads their genetic information, the server receives the data and stores it in a secure database. When saving, a checksum is calculated to verify the integrity of the data, confirming that the data was saved correctly.

[0273] The server then uses gene sequencing software, such as Illumina's BaseSpace Sequence Hub, to analyze the stored genetic information. The server adds the analysis job to a queue and waits for the analysis to complete. Once the analysis is complete, the results are retrieved.

[0274] Based on the analysis results, the server generates a prompt. For example, if the analysis results indicate that a particular gene may increase the implantation rate, the server generates the following prompt:

[0275] "Based on the user's genetic information, we have determined that certain genes may increase implantation rates. Based on this information, we would like to suggest the most appropriate treatment for the user."

[0276] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-4 (registered trademark)). The transmission is done via an API. Based on the received prompt, the generative AI model generates a sentence proposing the optimal treatment for the user. For example, it may generate a suggestion such as, "According to the analysis results, a specific gene may increase the implantation rate. Therefore, we recommend that you consider treatment targeting this gene when undergoing in vitro fertilization or intracytoplasmic sperm injection."

[0277] Finally, the server provides the suggestions received from the generative AI model to the user, who can review the suggestions through a web interface. The server can also send email or push notifications to notify the user of the suggestions.

[0278] This system allows users to upload genetic information from home, and the server analyzes the information to suggest optimal treatments. This eliminates the need for hospital visits, reduces the time and financial burden, and maximizes the effectiveness of treatment. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[0279] Step 1:

[0280] Users upload genetic information.

[0281] Users upload genetic information through a dedicated web interface. Specifically, users open a web browser, access the system's web interface, and log in by entering their username and password on the login screen. They then go to the genetic information upload page, click the "Select File" button, select a genetic information file from their local computer, and click the "Upload" button. The input is the genetic information file, and the output is the file being sent to the server.

[0282] Step 2:

[0283] The server receives and stores the genetic information.

[0284] The server receives the genetic information uploaded by the user. The received data is stored in a secure database. When saving, a checksum is calculated to verify the integrity of the data, and it is confirmed that the data was saved correctly. The input is the genetic information file sent by the user, and the output is the genetic information stored in the database.

[0285] Step 3:

[0286] The server analyzes the genetic information.

[0287] The server uses gene sequencing software to analyze the stored genetic information. Specifically, the server utilizes gene sequencing technologies such as Illumina's BaseSpace Sequence Hub. The server adds analysis jobs to a queue and waits until the analysis is complete. Once the analysis is complete, the server retrieves the results. The input is the genetic information stored in the database, and the output is the analysis results.

[0288] Step 4:

[0289] The server generates a prompt based on the analysis results.

[0290] The server generates a prompt sentence based on the analysis results. For example, if the analysis results indicate that a specific gene may increase the implantation rate, the server generates a prompt sentence such as, "Based on the user's genetic information, it has been determined that a specific gene may increase the implantation rate. Based on this information, please suggest the optimal treatment for the user." The input is the analysis results, and the output is the generated prompt sentence.

[0291] Step 5:

[0292] The server sends a prompt to the generative AI model.

[0293] The server sends the generated prompt sentence to the generative AI model. The transmission is done through an API. The server waits for a response from the generative AI model. The input is the generated prompt sentence, and the output is the transmission of the prompt sentence to the generative AI model.

[0294] Step 6:

[0295] A generative AI model generates suggestions.

[0296] Based on the received prompt, the generative AI model generates a sentence proposing the optimal treatment to the user. For example, it may generate a suggestion such as, "According to the analysis results, a specific gene may increase the implantation rate. Therefore, we recommend that you consider treatment targeting this gene when undergoing in vitro fertilization or intracytoplasmic sperm injection." The input is the prompt, and the output is the generated suggestion.

[0297] Step 7:

[0298] The server provides the suggestions to the user.

[0299] The server provides the suggestions received from the generative AI model to the user. The user can review the suggestions through a web interface. The server can also send email or push notifications to notify the user of the suggestions. The input is the generated suggestions, and the output is providing the suggestions to the user.

[0300] (Application example 3)

[0301] 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."

[0302] In conventional IVF and ICSI, genetic testing was performed to increase implantation rates, but there was a lack of health risk assessment and preventative measures. Furthermore, there were also insufficient methods for finding optimal timing therapy or for eliminating the need for hospital visits. This made it difficult for users to comprehensively understand their own health status and take appropriate treatments and preventative measures.

[0303] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for inputting data on men and women and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization, intracytoplasmic sperm injection, etc., means for analyzing genetic information and assessing health risks, and means for generating a health risk report. This enables users to assess their health risks based on their own genetic information and take appropriate treatments or preventive measures.

[0304] "Gender data" refers to data about men and women, including information on age, health status, lifestyle habits, etc.

[0305] The "optimal solution for timing therapy" is a solution for administering treatment at the most effective timing based on data for men and women.

[0306] "Means for eliminating the need for hospital visits for timing therapy" refers to means for enabling a user to perform timing therapy at home without visiting a hospital.

[0307] "Genes that increase implantation rates" are genes that have the potential to improve the implantation rate of embryos in in vitro fertilization and intracytoplasmic sperm injection.

[0308] "Means for analyzing genetic information" refers to means for analyzing a DNA sample provided by a user and obtaining genetic data.

[0309] "Means for assessing health risks" refers to means for assessing the health risks of users based on the acquired genetic data.

[0310] The "means for generating a health risk report" refers to a means for generating a health risk report for a user based on the health risk assessment results.

[0311] The system for implementing this invention is composed of three main components: a server, a terminal, and a user. The role and specific processing of each component will be explained below.

[0312] server

[0313] The server plays a central role in analyzing genetic information, assessing health risks, and generating health risk reports. The following software is installed on the server:

[0314] Gene Sequencing Library (GeneSequencer): Analyzes DNA samples provided by users to obtain genetic data.

[0315] Health Risk Analysis Library (HealthRiskAnalyzer): Evaluates the user's health risk based on the acquired genetic data.

[0316] The server receives the user's DNA sample and analyzes the genetic information using a gene sequencing library. Next, it evaluates the health risk from the analyzed genetic information using a health risk analysis library. Finally, it generates a health risk report based on the evaluation results and provides it to the user.

[0317] Terminal

[0318] The terminal provides an interface for users to provide genetic information and receive health risk reports. The terminal includes the following features:

[0319] Uploading DNA samples: Users upload their own DNA samples to the server via their devices.

[0320] Display Health Risk Report: Displays the health risk report sent from the server to the user.

[0321] User

[0322] Users provide their DNA sample through the terminal and receive a health risk report. Users use the system by following the steps below:

[0323] 1. Use the terminal to upload your DNA sample to the server.

[0324] 2. The server analyzes the genetic information and assesses health risks.

[0325] 3. The health risk report generated by the server is received and displayed on the terminal.

[0326] Specific examples

[0327] For example, when a user uploads their DNA sample using a device, the server analyzes the genetic information using a gene sequencing library. Then, using a health risk analysis library, the server evaluates the risk of certain diseases (e.g., diabetes, heart disease, cancer, etc.) based on the analyzed genetic information. Finally, the server generates a health risk report and sends it to the user's device. The user can view this report through their device and receive appropriate preventive measures and suggestions for improving their lifestyle habits.

[0328] Prompt Sentence Examples

[0329] "User uploads DNA sample. Use genetic sequencing technology to identify genes with specific health risks and generate a health risk report."

[0330] The above is an embodiment of the present invention.

[0331] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0332] Step 1:

[0333] A user uploads a DNA sample using a terminal. The user provides their own DNA sample as input data through the terminal interface. The terminal sends this DNA sample to a server. The input is the DNA sample data, and the output is the data sent to the server.

[0334] Step 2:

[0335] The server analyzes the DNA sample using a gene sequencing library (GeneSequencer). The server uses the received DNA sample as input data and obtains genetic information using gene sequencing technology. The input is DNA sample data, and the output is analyzed genetic information.

[0336] Step 3:

[0337] The server analyzes genetic information using a health risk analysis library (HealthRiskAnalyzer) and evaluates health risks. The server uses the acquired genetic information as input data and performs data processing and calculations to evaluate health risks. The input is genetic information, and the output is the health risk assessment results.

[0338] Step 4:

[0339] The server generates a health risk report. The server uses the health risk assessment results as input data to generate a health risk report for the user. The input is the health risk assessment results, and the output is the health risk report.

[0340] Step 5:

[0341] The server sends the generated health risk report to the terminal. The server sends the generated health risk report as input data to the user's terminal. The input is the health risk report, and the output is data sent to the terminal.

[0342] Step 6:

[0343] The terminal displays the health risk report to the user. The terminal uses the health risk report received from the server as input data and displays it to the user. The input is the health risk report, and the output is the display to the user.

[0344] 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.

[0345] "Example 1"

[0346] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system includes an emotion engine that analyzes emotions from the user's voice, facial expressions, behavioral patterns, etc. Specifically, the system recognizes basic emotions such as joy, anger, sadness, and surprise from the voice of the user when they speak to the system. The system also captures the user's facial expressions with a camera or other sensor and reads emotions from subtle changes in facial expressions. Furthermore, the system infers emotions from the user's behavioral patterns, such as smartphone operation patterns and daily rhythms.

[0347] "Example 2"

[0348] The analysis results of the emotion engine are fed back to the means for obtaining the optimal solution for timing therapy. Specifically, when the user's emotional state is good, that is, when joy or a sense of relief is high, timing therapy visits are recommended. Conversely, when the user's emotional state is unstable, that is, when anger or sadness is high, a suggestion is made to refrain from timing therapy visits. This makes it possible to provide the optimal timing therapy according to the user's emotional state.

[0349] "Example 3"

[0350] The emotion engine also works with a method to identify genes that increase the implantation rate in IVF and ICSI. Specifically, when the user's emotional state is good, it suggests the timing of IVF and ICSI. This shows that the user's emotional state has a positive effect on the implantation rate.

[0351] When there is a possibility of a cure, it becomes possible to proactively proceed with treatment.

[0352] The processing flow of each embodiment will be described below.

[0353] "Example 1"

[0354] Step 1: The user speaks to the system, and the system records the user's voice.

[0355] Step 2: The recorded audio is sent to the emotion engine, which recognizes basic emotions from the audio.

[0356] Step 3: At the same time, the user's facial expressions are captured by a camera or other sensor, and emotions are read from subtle changes in facial expressions.

[0357] Step 4: Furthermore, emotions are inferred from the user's behavioral patterns, such as smartphone operation patterns and daily routines.

[0358] "Example 2"

[0359] Step 1: The analysis results of the emotion engine are fed back to the means for obtaining the optimal solution for timing therapy.

[0360] Step 2: When the user's emotional state is good, i.e., when feelings of joy and relief are high, recommend a timing therapy visit.

[0361] Step 3: Conversely, when the user's emotional state is unstable, i.e., when anger or sadness is high, it is suggested to refrain from visiting the timing therapy clinic.

[0362] "Example 3"

[0363] Step 1: The emotion engine will also work with methods to identify genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection.

[0364] Step 2: When the user's emotional state is good, the timing of IVF / ICI is suggested.

[0365] Example 1

[0366] 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."

[0367] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit a hospital, placing a significant burden on them. Furthermore, while it is known that emotional changes affect the effectiveness of treatment, there has been a lack of means to appropriately analyze this and provide feedback. Furthermore, there is a need for a system that can efficiently collect and analyze information such as the user's age, health condition, and lifestyle.

[0368] 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.

[0369] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc., means for analyzing emotions from the user's voice, facial expressions, behavioral patterns, etc., means for registering information such as the user's age, health condition, and lifestyle in a database, means for converting voice data to text using voice recognition software, means for analyzing facial expression data using face recognition software, means for collecting and analyzing the user's behavioral patterns, and means for providing feedback on the results of the emotion analysis to the user. This reduces the burden of outpatient visits and enables emotion analysis and feedback to improve the effectiveness of treatment.

[0370] "Gender data" refers to information such as the user's age, health condition, and lifestyle.

[0371] "Timing therapy" refers to a treatment that aims to find the optimal timing for reproduction in men and women.

[0372] "Means that eliminate the need for hospital visits" refers to means by which users can receive the necessary information and treatment through the system without having to visit a medical institution.

[0373] "In vitro fertilization / microinjection" refers to a reproductive technique in which an egg and sperm are fertilized outside the body and the fertilized egg is then returned to the uterus.

[0374] The "means for identifying genes that increase the implantation rate" refers to a means for identifying genes that increase the implantation rate of fertilized eggs using gene sequencing technology.

[0375] "Means for analyzing emotions" refers to technology for analyzing emotions from the user's voice, facial expressions, behavioral patterns, etc.

[0376] "Means for registering in a database" refers to technology for storing information such as a user's age, health condition, lifestyle habits, etc. in a database.

[0377] "Speech recognition software" means software for converting voice data into text.

[0378] "Facial recognition software" refers to software used to analyze facial expression data captured by a camera.

[0379] "Means for collecting and analyzing behavioral patterns" refers to technology for collecting and analyzing data such as users' smartphone operation patterns and lifestyle rhythms.

[0380] "Means for providing feedback on emotion analysis results" refers to technology for notifying users of the results of the analyzed emotions and providing appropriate advice and information.

[0381] MODE FOR CARRYING OUT THE INVENTION

[0382] This invention is a system that collects information on the user's age, health condition, lifestyle, etc., and performs emotion analysis to provide optimal solutions for timing therapy, in vitro fertilization, and intracytoplasmic sperm injection. Specific embodiments of this system are described below.

[0383] Entering user information and registering

[0384] Users enter information such as their age, health status, and lifestyle habits through a dedicated interface. This interface is provided as a web browser or smartphone app. For example, a user opens a smartphone app and enters their age and health status. The device then sends the information entered by the user to a server. The server then registers the received data in a relational database management system (RDBMS) such as MySQL or PostgreSQL.

[0385] Audio data collection and analysis

[0386] When a user speaks to the system, the device collects voice data. The server converts this voice data into text using voice recognition software such as Google® Cloud Speech-to-Text API or IBM Watson® Speech to Text. For example, if a user says, "I'm very tired today," the speech is converted into text.

[0387] Facial expression data collection and analysis

[0388] The device uses a camera to capture the user's facial expression. The server uses facial recognition software such as OpenCV or Microsoft® Azure® Face API to analyze subtle changes in facial expression. For example, if the user looks tired, that expression is captured by the camera and analyzed.

[0389] Collecting and analyzing behavioral patterns

[0390] The device collects data such as the user's smartphone operation patterns and daily rhythm. The server analyzes this data to understand the user's behavioral patterns. For example, if a user uses their smartphone late into the night every night, that data is collected and analyzed.

[0391] Comprehensive analysis and feedback of emotions

[0392] The server comprehensively analyzes voice data, facial expression data, and behavioral pattern data to infer the user's emotions. The analysis results are fed back to the user. For example, if a user says, "I'm very tired today," and has a tired expression, the server will recognize the emotion of "fatigue" and provide feedback to the user saying, "Take it easy and rest today."

[0393] Examples and prompts

[0394] For example, a user opens a smartphone app and enters their age as "30," their health condition as "good," and their lifestyle as "I exercise every day." If the user then says, "I'm very tired today," the voice is converted into text, their facial expressions are captured by a camera, and their behavioral patterns are collected. The server analyzes this data, recognizes the emotion of "fatigue," and provides feedback to the user, saying, "Take it easy and rest today."

[0395] Examples of prompts to input to a generative AI model might include:

[0396] If a user says, "I feel very tired today," the speech recognition software converts this speech into text and the emotion engine recognizes it as "tired." If the user's facial expression looks tired, the facial recognition software detects this and the emotion engine reinforces the emotion of "tired."

[0397] In this way, the system can comprehensively grasp the user's state based on the user's input data and emotion analysis results.

[0398] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0399] Program processing flow

[0400] Step 1: Enter your user information

[0401] Users enter information such as their age, health condition, lifestyle habits, etc. through a dedicated interface. For example, a user opens a smartphone app and enters their age as "30," their health condition as "good," and their lifestyle habits as "I exercise every day."

[0402] Input: information such as age, health condition, lifestyle habits, etc.

[0403] Output: User information entered

[0404] Step 2: Submit and register data

[0405] The terminal sends the information entered by the user to the server, which then registers the received data in a relational database management system (RDBMS) such as MySQL or PostgreSQL.

[0406] Input: User information

[0407] Output: User information registered in the database

[0408] Step 3: Collecting audio data

[0409] The device collects voice data when the user speaks to the system, for example, when the user says, "I'm very tired today."

[0410] Input: User's voice

[0411] Output: Collected audio data

[0412] Step 4: Analyzing the audio data

[0413] The server converts the collected voice data into text using voice recognition software such as Google Cloud Speech-to-Text API or IBM Watson Speech to Text.

[0414] Input: Audio data

[0415] Output: Text data

[0416] Step 5: Collect facial expression data

[0417] The device uses a camera to capture the user's facial expression. For example, if the user looks tired, the camera will capture that expression.

[0418] Input: User's facial expression

[0419] Output: Collected facial expression data

[0420] Step 6: Analyze facial expression data

[0421] The server uses facial recognition software such as OpenCV and Microsoft Azure Face API to analyze subtle changes in facial expressions.

[0422] Input: facial expression data

[0423] Output: Analyzed facial expression data

[0424] Step 7: Collecting Behavioral Patterns

[0425] The device collects data on the user's smartphone operation patterns, lifestyle rhythms, etc. For example, if the user operates the smartphone late into the night every night, that data is collected.

[0426] Input: Data on smartphone operation patterns, daily rhythms, etc.

[0427] Output: Collected behavioral pattern data

[0428] Step 8: Analyze behavioral patterns

[0429] The server analyzes the collected behavioral pattern data and understands the user's behavioral patterns.

[0430] Input: Behavioral pattern data

[0431] Output: Analyzed behavioral pattern data

[0432] Step 9: Comprehensive emotional analysis

[0433] The server comprehensively analyzes voice data, facial expression data, and behavioral pattern data to infer the user's emotions.

[0434] Input: Text data, analyzed facial expression data, analyzed behavior pattern data

[0435] Output: Inferred emotion data

[0436] Step 10: Feedback

[0437] The server then provides the analysis results as feedback to the user. For example, if a user says, "I'm very tired today," and has a tired expression, the server will recognize the emotion of "fatigue" and provide feedback to the user saying, "Take a good rest today."

[0438] Input: Inferred emotion data

[0439] Output: Feedback to the user

[0440] In this way, the system can comprehensively grasp the user's state based on the user's input data and emotion analysis results.

[0441] (Application example 1)

[0442] 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."

[0443] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit a hospital, placing a significant burden on them. Highly accurate technology was also required for genetic identification. Furthermore, there was no system capable of analyzing a user's emotional state in real time and detecting abnormal emotional states, which could result in delayed response in emergencies. To solve these issues, a system that comprehensively manages a user's health and emotional state and responds appropriately is needed.

[0444] 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.

[0445] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection, means for analyzing the user's emotional state and detecting abnormal emotional states, and means for sending a notification to an emergency contact if an abnormal emotional state is detected.This makes it possible to comprehensively manage the user's health and emotional state and take appropriate measures.

[0446] "Gender data" refers to data about men and women, including information on age, health status, lifestyle habits, etc.

[0447] "Timing therapy" is a therapy that maximizes the therapeutic effect by administering treatment at a specific timing.

[0448] An "optimal solution" is the most effective or efficient solution under given conditions and constraints.

[0449] "Measures that eliminate the need for hospital visits" are measures that allow patients to receive treatment or diagnosis remotely without having to visit a medical institution in person.

[0450] "In vitro fertilization / microinsemination" is a reproductive technique in which an egg and sperm are fertilized outside the body, and then the fertilized egg is returned to the uterus.

[0451] "Implantation rate" is the probability that a fertilized egg will implant in the uterine lining.

[0452] A "means for distinguishing genes" is a means for identifying specific genes using gene sequencing technology.

[0453] "Emotional state" refers to the psychological state of the user that is analyzed from their voice, facial expressions, behavioral patterns, etc.

[0454] An "abnormal emotional state" is an emotional state that is different from normal, such as extreme stress or fear.

[0455] "Emergency Contacts" are contacts who should be notified in the event of an emergency.

[0456] The system for carrying out the present invention can comprehensively manage the health and emotional state of a user and take appropriate measures. The system is configured using the following hardware and software.

[0457] Hardware

[0458] Smartphone: Equipped with a camera and microphone, it captures the user's voice and facial expressions.

[0459] Server: processes and stores data.

[0460] software

[0461] OpenCV: A library for detecting faces and analyzing facial expressions from camera footage.

[0462] Keras: A deep learning framework for implementing emotion recognition models.

[0463] SpeechRecognition: A library for analyzing user speech.

[0464] smtplib: A library for sending emails to emergency contacts when abnormal emotional states are detected.

[0465] Data processing and calculation

[0466] 1. Camera footage capture: Use your smartphone camera to capture footage in real time.

[0467] 2. Face detection and expression analysis: Detect faces using OpenCV and analyze facial expressions using Keras emotion recognition models.

[0468] 3. Speech Analysis: Analyze the user's speech using the SpeechRecognition library to infer their emotional state.

[0469] 4. Abnormal Emotion Detection: If an abnormal emotion such as fear is detected, send an email notification to emergency contacts using smtplib.

[0470] Specific examples

[0471] For example, if a user is walking alone late at night and suddenly feels scared, the system will detect that emotion and send an email to emergency contacts saying, "User is feeling scared."

[0472] Prompt Sentence Examples

[0473] "Your goal is to develop a security application that analyzes the user's emotional state in real time and sends a notification to emergency contacts if an abnormal emotional state is detected. Specifically, you will analyze emotions using the smartphone's camera and microphone, and implement a function that sends a notification by email if an abnormal emotion such as fear is detected."

[0474] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0475] Step 1:

[0476] Initialize your smartphone camera and capture video in real time.

[0477] Input: Smartphone camera image

[0478] Output: Captured real-time video

[0479] Specific operation: Activates the smartphone camera and captures video frame by frame.

[0480] Step 2:

[0481] Detects faces from captured video and analyzes facial expressions.

[0482] Input: Captured real-time video

[0483] Output: Detected face regions and expression data

[0484] Specific operation: Detects faces using OpenCV and analyzes facial expressions using Keras emotion recognition model.

[0485] Step 3:

[0486] It uses the smartphone's microphone to capture the user's voice and analyzes it.

[0487] Input: User's voice

[0488] Output: Analyzed audio data and emotional state

[0489] What it does: Converts speech to text using the SpeechRecognition library and infers emotional state.

[0490] Step 4:

[0491] The user's emotional state is comprehensively analyzed from facial expression data and voice data.

[0492] Input: facial expression data and voice data

[0493] Output: Overall emotional state

[0494] Specific operation: Integrates facial expression data and voice data, and analyzes the overall emotional state using an emotion engine.

[0495] Step 5:

[0496] If an abnormal emotional state (e.g., fear) is detected, a notification will be sent to emergency contacts.

[0497] Input: Overall emotional state

[0498] Output: Notification to emergency contacts

[0499] What it does: If an abnormal emotional state is detected, it uses smtplib to send an email to emergency contacts.

[0500] Step 6:

[0501] Provide feedback to the user that a notification has been sent.

[0502] Input: Notification result to emergency contact

[0503] Output: Feedback to the user

[0504] What it does: Displays a message to the user informing them that the notification was sent successfully.

[0505] Example 2

[0506] 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."

[0507] Conventional timing therapy systems have difficulty predicting the ovulation date based on data on the user's menstrual cycle and body temperature fluctuations, and have not been able to provide optimal timing therapy that takes the user's emotional state into consideration. Therefore, there is a demand for providing optimal timing therapy that takes the user's physical condition and emotional state into account.

[0508] 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.

[0509] In this invention, the server includes a means for analyzing data on menstrual cycle and body temperature fluctuations to predict the day of ovulation, a means for analyzing emotional states to adjust the optimal solution for timing therapy, and a means for notifying the user based on the analysis results, thereby making it possible to provide the optimal timing therapy based on the user's physical condition and emotional state.

[0510] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[0511] "Timing therapy" is a therapy that promotes pregnancy by having sexual intercourse at the optimal time based on the day of ovulation.

[0512] The "means of eliminating the need for hospital visits" refers to a means that allows the user to perform timing therapy at home without visiting a medical institution.

[0513] "In vitro fertilization / microinsemination" is an assisted reproductive technique in which an egg and sperm are fertilized outside the body and the fertilized egg is then transplanted into the uterus.

[0514] A "gene that increases implantation rate" is a gene that increases the probability that a fertilized egg will implant in the uterine lining.

[0515] A "menstrual cycle" is the period from the start of a woman's menstruation to the start of the next menstruation.

[0516] "Body temperature fluctuation" refers to the phenomenon in which a woman's basal body temperature fluctuates with her menstrual cycle.

[0517] "Ovulation day" is the day an egg is released from the ovary.

[0518] "Emotional state" indicates the user's psychological state, and includes joy, relief, anger, sadness, and the like.

[0519] "Analysis results" are the results obtained by the server analyzing the data.

[0520] "Notification" is a means of communicating information to the user about analysis results and feedback.

[0521] This invention is a system that allows users to input data on their menstrual cycle and body temperature fluctuations and provides optimal timing therapy taking into account their emotional state. The system is composed of three entities: a server, a terminal, and the user.

[0522] First, the user uses a device such as a smartphone or PC to input data about their menstrual cycle and body temperature fluctuations into a dedicated application. For example, there is a form for entering the start date of menstruation and basal body temperature. The device then sends the input data to a server in real time.

[0523] The server stores the received data in a database. This database uses a common database management system. When storing the data, the server checks the consistency of the data to ensure that there is no invalid data.

[0524] The server then runs an algorithm to predict ovulation based on the stored data. This algorithm is implemented in Python and uses libraries such as Pandas and NumPy to process and analyze the data. For example, it calculates the average and standard deviation of the menstrual cycle and predicts ovulation.

[0525] The server calculates the optimal timing based on the analysis results. Specifically, it calculates the optimal timing within a few days before and after the day of ovulation and sends this information in JSON format to the device.

[0526] The user selects their current emotional state using a dedicated application. For example, there are options such as "joy," "relief," "anger," and "sadness." The device then transmits the emotional data to the server.

[0527] The server analyzes the user's emotional state using an emotion engine, which is built using a generative AI model. For example, if the emotional state is "joy," it assigns a high numerical score.

[0528] The server adjusts the optimal timing of therapy based on the analysis results of the emotion engine. For example, if the emotion score is high, it generates a message recommending hospital visits. Conversely, if the emotion score is low, it generates a message suggesting refraining from hospital visits.

[0529] Finally, the server sends the optimal timing and feedback to the device. The device then provides the user with information via push notification or email. For example, a notification might say, "The next optimal timing is XX / XX. Your emotional state is good, so we recommend that you visit the hospital."

[0530] For example, if a user inputs data on their menstrual cycle and body temperature fluctuations, and their emotional state is set as "joy," the server analyzes this data and calculates the optimal timing. Since their emotional state is good, a notification is sent to the user's device recommending that they visit a clinic for timing therapy.

[0531] An example prompt is, "Please enter your menstrual cycle and body temperature fluctuation data. Also, please select your current emotional state. For example, joy, relief, anger, sadness, etc."

[0532] The system allows users to receive optimally timed therapy based on their physical and emotional state.

[0533] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0534] Step 1:

[0535] The user inputs data about their menstrual cycle and body temperature fluctuations into the terminal. Specifically, they input the menstrual start date and basal body temperature into a form in a dedicated application. The input data is sent from the terminal to the server. The input data includes the menstrual start date, basal body temperature, etc. The output is the user data sent to the server.

[0536] Step 2:

[0537] The server stores the received data in a database. Specifically, it stores the data using a database management system. When storing the data, it checks the integrity of the data to ensure that there is no invalid data. The input is the user data sent from the terminal, and the output is the data stored in the database.

[0538] Step 3:

[0539] The server runs an algorithm to predict ovulation based on the stored data. Specifically, it uses a script implemented in Python and libraries such as Pandas and NumPy to process and analyze the data. For example, it calculates the average and standard deviation of the menstrual cycle and predicts ovulation. The input is user data retrieved from the database, and the output is the predicted ovulation date.

[0540] Step 4:

[0541] The server calculates the optimal timing based on the analysis results. Specifically, it calculates the optimal timing for several days before and after the ovulation date and sends that information in JSON format to the device. The input is the predicted ovulation date, and the output is the optimal timing information.

[0542] Step 5:

[0543] The user selects their current emotional state using a dedicated application. Specifically, they select from options such as "joy," "relief," "anger," and "sadness." The input is the user's emotional state, and the output is emotional data sent from the device to the server.

[0544] Step 6:

[0545] The server uses an emotion engine to analyze the user's emotional state. Specifically, it uses a generative AI model to analyze the emotional data. For example, if the emotional state is "joy," it sets a high numerical score. The input is the emotional data sent from the device, and the output is the analyzed emotional score.

[0546] Step 7:

[0547] The server adjusts the optimal timing solution for therapy based on the analysis results of the emotion engine. Specifically, if the emotion score is high, a message recommending hospital visits is generated. Conversely, if the emotion score is low, a message suggesting refraining from hospital visits is generated. The input is the analyzed emotion score, and the output is the adjusted optimal solution message.

[0548] Step 8:

[0549] The server sends the optimal timing and feedback to the device. Specifically, the information is provided via push notification or email. For example, a notification such as "The next optimal timing is XX month XX day. Your emotional state is good, so we recommend that you visit the hospital." The input is the adjusted optimal solution message, and the output is the notification sent to the user.

[0550] (Application example 2)

[0551] 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."

[0552] In conventional infertility treatments such as timing therapy, in vitro fertilization, and intracytoplasmic sperm injection, the optimal timing is calculated based on data on both men and women, and genetic analysis is performed, but recommendations for clinic visits and predictions of ovulation dates are not sufficiently performed taking into account the user's emotional state. As a result, it was not possible to provide the optimal treatment timing according to the user's emotional state, and improving the effectiveness of treatment was an issue.

[0553] 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.

[0554] In this invention, the server includes means for inputting male and female data and obtaining the optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection, means for predicting the day of ovulation from information on menstrual cycle and body temperature fluctuations, and means for analyzing the user's emotional state and recommending outpatient visits when the user is in a good mood and suggesting that the user refrain from outpatient visits when the user is in an unstable mood. This makes it possible to provide optimal treatment timing according to the user's emotional state.

[0555] "Gender data" refers to information about individuals, such as age, health status, and lifestyle habits.

[0556] "Timing therapy" refers to a treatment that increases the chances of pregnancy by having sexual intercourse at the optimal time according to a woman's ovulation cycle.

[0557] "Measures to eliminate the need for hospital visits" refers to methods or technologies that allow users to receive treatment or diagnosis at home without having to visit a medical institution.

[0558] "In vitro fertilization / microinjection" refers to a type of infertility treatment in which an egg and sperm are fertilized outside the body, and then the fertilized egg is returned to the uterus.

[0559] "Genes that increase implantation rate" refer to genes that are associated with increasing the probability of a fertilized egg implanting in the uterine lining.

[0560] "Gene sequencing technology" refers to the technology for analyzing the base sequence of DNA.

[0561] The term "menstrual cycle" refers to the period from the start of a woman's menstruation to the start of the next menstruation.

[0562] "Body temperature fluctuation" refers to the change in an individual's body temperature depending on time and circumstances.

[0563] "Ovulation" refers to the day an egg is released from a woman's ovaries.

[0564] "Emotional state" refers to an individual's emotional state or mood.

[0565] "Analysis" refers to examining data or information in detail to clarify its meaning and structure.

[0566] "Recommending medical treatment" means recommending that the user visit a medical institution.

[0567] "Avoid medical visits" refers to encouraging users to avoid going to medical institutions.

[0568] The system for carrying out this invention consists of three main elements: a server, a terminal, and a user. The server includes means for inputting male and female data and obtaining the optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization and intracytoplasmic sperm injection, means for predicting the day of ovulation from information on menstrual cycle and body temperature fluctuations, and means for analyzing the user's emotional state, recommending outpatient visits when the user is emotionally well, and suggesting that the user refrain from outpatient visits when the user is emotionally unstable.

[0569] The server uses Python and its libraries (NumPy, scikit-learn) to analyze data and make predictions. Specifically, it obtains data on the user's menstrual cycle, body temperature fluctuations, and emotional state, and uses this data to predict ovulation. It also analyzes emotional state and makes recommendations for medical visits or suggestions for avoiding them.

[0570] The terminal is a device used by the user, such as a smartphone or tablet, through which data is input and results are received through an application installed on the device. The user inputs data such as menstrual cycle, body temperature, and emotional state into the application.

[0571] As an example, a user enters the following data into an application:

[0572] Menstrual cycle: [28, 30, 27, 29, 31]

[0573] Body temperature fluctuations: [36.5, 36.7, 36.8, 36.6, 36.9]

[0574] Emotional state: [7, 8, 6, 5, 9]

[0575] The server uses this data to predict the next ovulation date and decides whether to recommend a visit based on the patient's emotional state. An example of a prompt to be input to the generative AI model is as follows:

[0576] User's menstrual cycle data: [28, 30, 27, 29, 31]

[0577] User's temperature fluctuation data: [36.5, 36.7, 36.8, 36.6, 36.9]

[0578] User emotional state data: [7, 8, 6, 5, 9]

[0579] Use this data to predict your next ovulation date and decide whether to recommend a visit based on your emotional state.

[0580] This system allows users to easily input data at home and find the optimal timing for treatment. It also recommends clinic visits based on emotional state, reducing stress for users and improving treatment effectiveness.

[0581] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0582] Step 1:

[0583] Users input data about their menstrual cycle, body temperature fluctuations, and emotional state into a smartphone application.

[0584] Input: Menstrual cycle data, body temperature fluctuation data, emotional state data

[0585] Output: User data is saved in the application

[0586] Step 2:

[0587] The terminal transmits the input data to the server.

[0588] Input: User data

[0589] Output: User data sent to the server

[0590] Step 3:

[0591] The server analyzes the data received and predicts the date of ovulation based on menstrual cycle and body temperature fluctuation data.

[0592] Input: Menstrual cycle data, body temperature fluctuation data

[0593] Output: Predicted ovulation date

[0594] How it works: The server uses Python's NumPy and scikit-learn libraries to build a linear regression model and predict the next ovulation date.

[0595] Step 4:

[0596] The server analyzes the emotional state data and recommends visiting the hospital when the emotions are good, and suggests refraining from visiting the hospital when the emotions are unstable.

[0597] Input: Emotional state data

[0598] Output: Recommendation to visit the hospital or suggestion to refrain from visiting the hospital

[0599] Specific operation: The server calculates the average emotional state, and if it exceeds a certain threshold, it recommends visiting the hospital, and if it is below the threshold, it suggests refraining from visiting the hospital.

[0600] Step 5:

[0601] The server sends the predicted ovulation date and recommendations for visiting the clinic to the device.

[0602] Input: Predicted ovulation date, recommended visit or avoidance of visit

[0603] Output: Result data sent to the terminal

[0604] Step 6:

[0605] The terminal notifies the user of the result data received from the server.

[0606] Input: Result data

[0607] Output: Predicted ovulation date and doctor visit suggestions displayed to the user

[0608] Specific operation: The device notifies the user through the application of the predicted ovulation date and suggestions for visiting the clinic.

[0609] This series of processes allows users to easily input data at home and find the optimal timing for treatment. In addition, by recommending hospital visits based on emotional state, it is possible to reduce user stress and improve treatment effectiveness.

[0610] Example 3

[0611] 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."

[0612] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit a clinic, placing a significant time and financial burden on the user. Furthermore, genetic screening to increase implantation rates has not been adequately performed, limiting the effectiveness of treatment. Furthermore, it is known that the user's emotional state affects the success of treatment, but no treatment timing suggestions have been made that take this into account. There is a need to resolve these issues and provide more effective and efficient infertility treatments.

[0613] 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.

[0614] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc., means for analyzing the user's genetic information and identifying genes that may increase implantation rates, and means for monitoring the user's emotional state and suggesting the timing of treatment when the user's emotional state is good. This allows the user to receive optimal treatment based on their genetic information while reducing the burden of outpatient visits, and also enables treatment to be carried out at a timing that takes their emotional state into consideration.

[0615] "Gender data" refers to information about men and women, such as age, health status, and lifestyle habits.

[0616] "Means for obtaining the optimal solution for timing therapy" refers to a method for calculating and proposing the optimal timing therapy based on data from men and women.

[0617] "Means for eliminating the need for hospital visits for timing therapy" refers to a method that allows a user to perform timing therapy at home without visiting a hospital.

[0618] "Means for identifying genes that increase implantation rates" refers to a method for identifying genes that may increase implantation rates using gene sequencing technology.

[0619] "Means for analyzing a user's genetic information" refers to a method for analyzing a genetic sample provided by a user and extracting genetic information.

[0620] "Means for identifying genes that may increase implantation rate" refers to a method for finding specific genes that may increase implantation rate from analyzed genetic information.

[0621] "Means for monitoring the user's emotional state" refers to a method for monitoring the user's emotional state in real time and collecting it as data.

[0622] The "means for suggesting the timing of treatment when the emotional state is good" refers to a method for suggesting the optimal timing of treatment when the emotional state of the user is determined to be good.

[0623] This invention is a system that proposes optimal fertility treatment methods and their timing through cooperation between users, servers, and terminals. Specific embodiments of this system are described below.

[0624] Hardware and software used

[0625] Hardware: High-performance servers, user devices (PCs, smartphones)

[0626] Software: Gene sequencing software, emotion engines, data analysis tools, generative AI models

[0627] System Overview

[0628] 1. Users provide genetic information

[0629] Users collect their own genetic samples using a dedicated genetic sample collection kit.

[0630] The user places the collected sample in a special container and sends it to the server using the specified method.

[0631] 2. The server analyzes the genetic information

[0632] The server analyzes the received genetic samples using genetic sequencing software (e.g., a common genetic sequencing tool).

[0633] The server processes the analysis results using data analysis tools (e.g., Python's Pandas library) to identify genes that may increase implantation rates.

[0634] 3. The server proposes a treatment method based on the analysis results

[0635] Based on the identified genetic information, the server suggests the most appropriate treatment for the user.

[0636] The server notifies the user terminal of the content of the proposal.

[0637] 4. The user confirms the proposed treatment.

[0638] The user receives a notification on their device and confirms the proposed treatment.

[0639] If necessary, the user will work with a medical institution to proceed with treatment.

[0640] 5. Emotion engine monitors the user's emotional state

[0641] The emotion engine monitors the user's emotional state in real time.

[0642] Users input their emotional state through a smartphone app.

[0643] 6. The server suggests the timing of treatment based on the patient's emotional state.

[0644] The server receives data from the emotion engine and, if it determines that the user's emotional state is good, suggests the timing of in vitro fertilization and intracytoplasmic sperm injection.

[0645] The server notifies the user terminal of the content of the proposal.

[0646] 7. The user proceeds with treatment based on the suggested timing

[0647] The user proceeds with treatment based on the suggested timing.

[0648] The user will work with the medical institution to schedule treatment.

[0649] Specific examples

[0650] Providing genetic information: The user uses a dedicated genetic sample collection kit and sends the sample to the server.

[0651] Notification of analysis results: Based on the analysis results, the server notifies the user's device, saying, "Your genetic information has identified genes that may increase the implantation rate. The optimal treatment is ____."

[0652] Emotional state monitoring: When a user types "I'm feeling good today" into a smartphone app, the emotion engine analyzes this and sends it to the server.

[0653] Timing suggestion: The server notifies the user, "Your current emotional state is good. It is the best time for IVF / ICI."

[0654] Example prompts for generative AI models

[0655] "How can we analyze the genetic information provided by users and identify genes that may increase implantation rates?"

[0656] "Please explain how the system works to suggest the best time for IVF / ICI when the user's emotional state is good."

[0657] In this way, a system is constructed in which the user, the server, and the terminal work together to propose the optimal treatment and timing. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0658] Step 1:

[0659] The user provides genetic information.

[0660] Input: User's genetic sample (e.g., saliva or blood)

[0661] Specific operation: The user uses a dedicated gene sample collection kit to collect their own gene sample, places the collected sample in a dedicated container, and sends it to the server using a specified method.

[0662] Output: Genetic sample sent to the server

[0663] Step 2:

[0664] The server analyzes the genetic information.

[0665] Input: Genetic sample submitted by the user

[0666] Specific operation: The server launches the gene sequencing software (e.g., a general gene sequencing tool), inputs the sample data, and begins analysis.

[0667] Data processing: Gene sequencing software analyzes the genetic information and generates genetic sequence data.

[0668] Output: Analyzed gene sequence data

[0669] Step 3:

[0670] The server will suggest a treatment method based on the analysis results.

[0671] Input: Analyzed gene sequence data

[0672] Specific operation: The server uses data analysis tools (e.g., Python's Pandas library) to process gene sequence data and identify genes that may increase implantation rates.

[0673] Data calculation: Data analysis tools analyze gene sequence data and extract specific genes.

[0674] Output: Genetic information that may increase implantation rate

[0675] Step 4:

[0676] The server notifies the user terminal of the proposed treatment.

[0677] Input: Genetic information that may increase implantation rate

[0678] Specific operation: The server generates a message saying, "Your genetic information has identified a gene that may increase the implantation rate. The optimal treatment is ____," and sends it to the user's device.

[0679] Output: Treatment proposal sent to the user's device

[0680] Step 5:

[0681] The user confirms the proposed treatment.

[0682] Input: Treatment proposal sent to user device

[0683] Specific operation: The user receives a notification on their device, confirms the proposed treatment, and, if necessary, works with a medical institution to proceed with the treatment.

[0684] Output: User's treatment selection and implementation

[0685] Step 6:

[0686] An emotion engine monitors the user's emotional state.

[0687] Input: User emotional state data

[0688] How it works: Users input their emotional state through a smartphone app, which the emotion engine analyzes and sends to the server.

[0689] Data processing: The emotion engine analyzes the user's emotional state data and evaluates the emotional state.

[0690] Output: Emotional state assessment data sent to the server

[0691] Step 7:

[0692] The server suggests timing of treatment based on emotional state.

[0693] Input: Emotional state assessment data

[0694] Specific operation: The server receives data from the emotion engine, and if it determines that the user's emotional state is good, it suggests the timing of IVF / ICI.

[0695] Data calculation: The server analyzes the emotional state assessment data and calculates the optimal timing for treatment.

[0696] Output: Treatment timing suggestions sent to the user device

[0697] Step 8:

[0698] The user proceeds with treatment based on the suggested timing.

[0699] Input: Treatment timing suggestions sent to the user's device

[0700] What happens: The user sees the notification and contacts their healthcare provider to schedule a treatment appointment.

[0701] Output: Confirm and execute the user's treatment schedule.

[0702] (Application example 3)

[0703] 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."

[0704] In conventional IVF and ICSI, genetic screening and timing optimization to increase implantation rates have not been adequately performed. Additionally, there is no system that comprehensively evaluates the user's health risks and emotional state and provides an optimal health management plan, making it difficult to maximize the effectiveness of treatment.

[0705] 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.

[0706] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection, means for analyzing genetic information and identifying health risks, means for monitoring emotional states and evaluating stress levels, and means for comprehensively evaluating health and emotional states and providing an optimal health management plan. This makes it possible to comprehensively evaluate a user's genetic information and emotional state and provide the optimal treatment and health management plan.

[0707] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[0708] "Timing therapy" is a method of administering treatment at the optimal timing based on data for men and women.

[0709] "Methods that eliminate the need for hospital visits" are methods that allow users to receive treatment or diagnosis remotely without having to visit a medical institution.

[0710] The "means for identifying genes that increase implantation rates" refers to a means for identifying genes that have the potential to improve implantation rates in in vitro fertilization or intracytoplasmic sperm injection using gene sequencing technology.

[0711] "Means for analyzing genetic information and identifying health risks" refers to means for analyzing genetic information provided by users and identifying health risks such as heart disease and diabetes.

[0712] The "means for monitoring emotional state and assessing stress level" refers to a means for monitoring a user's emotional state in real time and assessing the level of stress or anxiety.

[0713] "Means for comprehensively evaluating health status and emotional state and providing an optimal health management plan" refers to a means for comprehensively evaluating a user's genetic information and emotional state and proposing an optimal health management plan.

[0714] A system for implementing this invention includes a server, a user terminal, and various sensor devices. The server analyzes genetic information provided by the user and identifies health risks. It also monitors the user's emotional state and evaluates their stress level. The system comprehensively evaluates this data and provides an optimal health management plan.

[0715] Hardware and software used

[0716] Hardware: smartphones, gene sequencing devices, emotional state monitoring sensors

[0717] Software: Python, gene sequencing analysis tool, emotional state monitoring tool

[0718] Data processing and calculation

[0719] 1. Genetic analysis:

[0720] The server receives the genetic information provided by the user and analyzes it using a gene sequencing analysis tool, identifying health risks such as heart disease and diabetes from the analysis results.

[0721] 2. Emotional state monitoring:

[0722] The user terminal collects the user's emotional state in real time using an emotional state monitoring sensor, and the collected data is sent to a server, where the stress level is evaluated using an emotional state monitoring tool.

[0723] 3. Providing Health Care Plans:

[0724] The server comprehensively evaluates the genetic information and emotional state data to generate an optimal health management plan, which is then sent to the user's smartphone.

[0725] Specific examples

[0726] For example, if a user uploads their genetic information to the app, the server will identify their risk of heart disease and diabetes. If the emotional state monitoring function assesses the user's stress level as "high," the server will notify the user, "Suggesting yoga or meditation to help you relax."

[0727] Prompt Sentence Examples

[0728] Analyze your users' genetic information to identify health risks, monitor their emotional state, assess their stress levels, and use this information to suggest optimal health management plans.

[0729] In this way, a specific embodiment for carrying out the invention has been shown, and it has been made clear how the user can use this system.

[0730] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0731] Step 1:

[0732] Users upload their genetic information to a smartphone app.

[0733] Input: User's genetic information file

[0734] Output: Genetic information is sent to the server

[0735] Specific operation: The user selects a genetic information file and presses the upload button in the app, which then sends the genetic information to the server.

[0736] Step 2:

[0737] The server receives the genetic information and performs analysis using a genetic sequencing analysis tool.

[0738] Input: Genetic information sent to the server

[0739] Output: Health risk identification results

[0740] How it works: The server inputs genetic information into an analysis tool to identify health risks such as heart disease and diabetes. The analysis results are stored in a database.

[0741] Step 3:

[0742] The user terminal collects the user's emotional state using an emotional state monitoring sensor.

[0743] Input: Real-time emotional state data of the user

[0744] Output: Emotional state data is sent to the server

[0745] Specific operation: The user terminal periodically collects data from the emotional state monitoring sensor and transmits it to the server.

[0746] Step 4:

[0747] A server receives the emotional state data and assesses stress levels using an emotional state monitoring tool.

[0748] Input: Emotional state data sent to the server

[0749] Output: Stress level evaluation result

[0750] Specific operation: The server inputs emotional state data into the monitoring tool and evaluates stress levels. The evaluation results are stored in a database.

[0751] Step 5:

[0752] The server comprehensively evaluates genetic information and emotional state data to generate an optimal health management plan.

[0753] Input: Health risk identification results, stress level assessment results

[0754] Output: Health management plan

[0755] How it works: The server integrates genetic information and emotional state data, and uses a generative AI model to generate an optimal health management plan. The plan is then stored in a database.

[0756] Step 6:

[0757] The server notifies the user's smartphone of the generated health management plan.

[0758] Input: Health Care Plan

[0759] Output: Health management plan displayed on the user's smartphone

[0760] Specific operation: The server sends the health management plan to the user's smartphone, and the app displays the plan.

[0761] 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.

[0762] 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.

[0763] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0764] 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.

[0765] [Second embodiment]

[0766] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0767] 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.

[0768] 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).

[0769] 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.

[0770] 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.

[0771] 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).

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0778] "Example 1"

[0779] One form of the present invention is a system that registers information such as age, health status, and lifestyle habits into a database as a means of inputting data on men and women. Specifically, users enter their own information through a dedicated interface, and the system collects and analyzes it.

[0780] "Example 2"

[0781] Additionally, as a means of finding the optimal solution for timing therapy, an algorithm will be implemented to calculate the optimal timing based on the collected data. Specifically, the system will predict the day of ovulation based on information such as a woman's menstrual cycle and body temperature fluctuations, and notify the user of the optimal timing based on that.

[0782] "Example 3"

[0783] Furthermore, gene sequencing technology is used as a means of identifying genes that increase implantation rates in IVF and ICSI. Specifically, the genetic information provided by the user is analyzed to identify genes that have the potential to increase implantation rates. This makes it possible to propose the most suitable treatment for each user.

[0784] The processing flow of each embodiment will be described below.

[0785] "Example 1"

[0786] Step 1: The user enters information such as their age, health condition, and lifestyle habits through a dedicated interface.

[0787] Step 2: The system registers the information provided by the user in a database.

[0788] Step 3: The system analyzes the registered information and suggests the most appropriate treatment for the user.

[0789] "Example 2"

[0790] Step 1: The system uses information provided by the user to predict the date of ovulation based on the woman's menstrual cycle, body temperature fluctuations, etc.

[0791] Step 2: The system calculates the optimal timing based on the predicted ovulation date.

[0792] Step 3: The system notifies the user of the optimal timing.

[0793] "Example 3"

[0794] Step 1: The system collects genetic information provided by the user.

[0795] Step 2: The genetic information collected by the system is analyzed using gene sequencing technology.

[0796] Step 3: The system uses the analysis results to identify genes that may increase implantation rates and suggests the most appropriate treatment for the user.

[0797] Example 1

[0798] 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."

[0799] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit the hospital, placing a significant burden on them both in terms of time and money. Genetic identification to increase implantation rates is also difficult, making it difficult to find the optimal treatment. Furthermore, there is a lack of efficient means to collect and analyze information such as the patient's age, health condition, and lifestyle, making personalized treatment difficult.

[0800] 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.

[0801] In this invention, the server includes: a means for the user to input information such as age, health condition, and lifestyle habits through a dedicated interface; a means for the terminal to send the input data to the server; a means for the server to register the data in a database; a means for the server to collect and analyze the data; a means for the server to input the analysis results into a generative AI model; and a means for the server to return the results of the generative AI model to the user. This allows patients to input their own information and find the optimal treatment without having to visit a hospital. It also enables efficient genetic identification and the provision of personalized treatments.

[0802] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[0803] "Timing therapy" is a therapy that maximizes the therapeutic effect by administering treatment at a specific timing.

[0804] "Measures that eliminate the need for hospital visits" are measures that allow patients to receive treatment or diagnosis remotely without having to visit a medical institution in person.

[0805] "In vitro fertilization / microinsemination" is an assisted reproductive technique in which sperm and eggs are fertilized outside the body, and the fertilized eggs are then transplanted into the uterus.

[0806] The "means for identifying genes that increase implantation rates" refers to means for identifying specific genes that increase implantation rates using gene sequencing technology.

[0807] A "dedicated interface" is a specific input screen or application designed for users to enter their information.

[0808] A "terminal" is a device (e.g., a smartphone or PC) that a user uses to input information and send it to a server.

[0809] "Server" means a computer system that receives, processes, stores, and analyzes data sent by users.

[0810] A "database" is a system for efficiently storing and managing collected data.

[0811] A "generative AI model" is an artificial intelligence model that generates analytical results based on input data.

[0812] A "prompt" is an instruction to be input into a generative AI model, and is a statement used to perform a specific analysis or generation.

[0813] This invention is a system that allows users to input information such as age, health status, and lifestyle habits through a dedicated interface, registers the information in a database, and collects and analyzes it.The system then inputs the analysis results into a generative AI model, and returns the generated results to the user, providing optimal treatment.

[0814] Users enter their information using a dedicated interface (e.g., a web application or a mobile application). This interface is built using web technologies such as HTML, CSS, and JavaScript. The information entered by users includes their age, health status, lifestyle habits, etc.

[0815] The terminal (user's device) sends the entered data to the server as an HTTP request. The data is sent in JSON format. The server analyzes the received data and registers it in a database. The server receives and registers the data using frameworks such as Python's Flask or Django, or Node.js's Express. MySQL or PostgreSQL is used as the database.

[0816] After the data is registered in the database, the server collects and analyzes the data using data analysis libraries such as Python's pandas and scikit-learn. For example, it can analyze trends in the age distribution and health status of users.

[0817] The server inputs the analysis results into a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. A prompt sentence is generated and input into the model to obtain detailed analysis results. Examples of prompt sentences include the following:

[0818] Prompt: "If a 30-year-old user exercises daily and is in good health, analyze what health trends they exhibit compared to other users of the same age."

[0819] The server returns the results obtained from the generative AI model to the user. The results are displayed on the user's device. For example, HTML and JavaScript are used to display the results on a web page. The user can view the results through the web page.

[0820] As a concrete example, consider a scenario in which a user uses a dedicated web interface to enter the following information:

[0821] Age: 30

[0822] Health: Good

[0823] Lifestyle: Exercise every day

[0824] This information is sent to a server and registered in a database. The registered data is collected by the server and used to analyze age distribution and health trends. The analysis results are input into a generative AI model, which generates detailed results. For example, a result may be that "30-year-old users who exercise daily have a high rate of good health compared to their peers." The results are displayed on the user's device, where they can check them.

[0825] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0826] Step 1:

[0827] The user enters information

[0828] Using a dedicated interface, users enter information such as their age, health status, and lifestyle habits. Specifically, they open a web browser, access a dedicated web page, enter the required information in a form, and click the "Submit" button. The input data includes age (e.g., 30 years old), health status (e.g., good), and lifestyle habits (e.g., exercise daily).

[0829] Step 2:

[0830] The device sends the input data to the server

[0831] The terminal converts the data entered by the user into JSON format and sends it to the server as an HTTP POST request. Specifically, it sends the following JSON data:

[0832] json{"age": 30,"health_status": "good","lifestyle": "exercise daily"}

[0833] The input is the data entered by the user, and the output is the JSON data sent to the server.

[0834] Step 3:

[0835] The server registers the data in the database

[0836] The server parses the received JSON data and registers it in a database. Specifically, it uses frameworks such as Python's Flask or Django, or Node.js's Express to receive and register the data. MySQL or PostgreSQL is used as the database. The following SQL query is executed.

[0837] sql

[0838] INSERT INTO user_data (age, health_status, lifestyle) VALUES (30, 'Good', 'Exercise daily');

[0839] The input is the JSON data sent to the server, and the output is the data registered in the database.

[0840] Step 4:

[0841] The server collects and analyzes the data

[0842] The server retrieves all user data from the database and analyzes trends in age distribution and health status. Specifically, it uses data analysis libraries such as Python's pandas and scikit-learn. The following Python code is used:

[0843] python

[0844] import pandas as pd

[0845] Retrieving data from a database

[0846] data = pd.read_sql('SELECT FROM user_data', connection)

[0847] Age distribution analysis

[0848] age_distribution = data['age'].value_counts()

[0849] Analyze health trends

[0850] health_trends = data['health_status'].value_counts()

[0851] The input is all user data obtained from the database, and the output is the analysis results of age distribution and health status trends.

[0852] Step 5:

[0853] The server inputs the analysis results into the generative AI model

[0854] The server generates a prompt based on the analysis results and inputs it into the generative AI model. Specifically, it generates the following prompt:

[0855] Prompt: "If a 30-year-old user exercises daily and is in good health, analyze what health trends they exhibit compared to other users of the same age."

[0856] The input is the analysis results of age distribution and health status trends, and the output is a prompt sentence to be input into the generative AI model.

[0857] Step 6:

[0858] The server returns the results of the generated AI model to the user

[0859] The server returns the results obtained from the generative AI model to the user. Specifically, it uses HTML and JavaScript to display the results obtained from the generative AI model on the user's web page. For example, it displays the results as follows:

[0860] The result: "30-year-old users who exercise daily are more likely to be in good health than their peers."

[0861] The input is the result obtained from the generative AI model, and the output is the result displayed on the user's device.

[0862] (Application example 1)

[0863] 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."

[0864] Conventional timing therapy, IVF, and ICSI require patients to visit the hospital, which places a significant burden on them. Furthermore, optimal dietary plans based on individual health conditions and lifestyle habits were not proposed, and health management was not adequately implemented. This resulted in the problem of not maximizing the effectiveness of treatment.

[0865] 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.

[0866] In this invention, the server includes a means for inputting male and female data and obtaining an optimal solution for timing therapy, a means for eliminating the need for outpatient visits for timing therapy, a means for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc., and a means for collecting information such as the user's age, health condition, and lifestyle habits and proposing an individually optimized diet plan. This reduces the burden of outpatient visits for patients and makes it possible to maximize the effectiveness of treatment by proposing an optimal diet plan based on the individual's health condition and lifestyle habits.

[0867] "Gender data" refers to information about individuals such as age, gender, health status, and lifestyle habits.

[0868] "Timing therapy" refers to a medical technique that maximizes effectiveness by administering treatment at a specific time.

[0869] An "optimal solution" refers to the most effective solution given the conditions and constraints.

[0870] "Non-visit measures" refer to methods that allow patients to receive treatment or diagnosis without visiting a medical institution in person.

[0871] "In vitro fertilization / microinjection" refers to an assisted reproductive technique in which an egg and sperm are fertilized outside the body and the fertilized egg is then returned to the uterus.

[0872] "Genes that increase implantation rate" refer to genes that increase the probability of a fertilized egg implanting in the uterine lining.

[0873] "Genetic discrimination means" refers to a method for identifying a specific gene and confirming its presence or characteristics.

[0874] "Information on age, health status, lifestyle habits, etc." refers to data such as an individual's age, current health status, and daily lifestyle habits.

[0875] "Individually optimized meal plans" refer to customized meal suggestions based on an individual's health and lifestyle.

[0876] "Means of collection and analysis" refers to the methods of collecting data and analyzing it to derive useful information.

[0877] A system for implementing this invention includes a server, a user terminal, and a database. The server runs a program for collecting and analyzing data from users. The user terminal is a device such as a smartphone or PC, and provides an interface for users to input their information. The database stores the collected data and allows the server to access it as needed.

[0878] The server executes a program that includes the following means:

[0879] 1. Data collection methods:

[0880] The system collects information such as age, health status, and lifestyle habits sent from the user's device. The user inputs this information through a dedicated interface. For example, the user inputs their age and health status using a smartphone app.

[0881] 2. Data analysis methods:

[0882] The server analyzes the collected data and generates an individually optimized meal plan using a programming language such as Python. For example, if the user has diabetes, it will suggest a low-carb menu.

[0883] 3. Proposed method:

[0884] Based on the analysis results, the app will suggest the optimal meal plan for the user. The suggestion will be sent to the user's device. For example, when the user opens the app, the recommended meal plan will be displayed.

[0885] The specific hardware and software configuration is as follows:

[0886] Hardware: smartphones, PCs, servers

[0887] Software: Python, database management system (e.g., MySQL), user interface library (e.g., Tkinter)

[0888] The server stores the data entered by the user in a database in JSON format and analyzes it as needed. The analysis results are sent to the user's device, and the user can receive an optimal meal plan based on their health condition and lifestyle.

[0889] As a concrete example, the following prompt sentence can be input to a generative AI model:

[0890] Example prompt sentence:

[0891] Develop an application that suggests optimal meal plans based on the user's age, health condition, and lifestyle habits. The information entered by the user includes name, age, health condition (e.g., diabetes, high blood pressure, etc.), and lifestyle habits (e.g., lack of exercise, smoking, etc.). Implement a function that collects and analyzes this information to suggest individually optimized menus.

[0892] In this way, users can receive optimal meal plans based on their own health condition and lifestyle habits, enabling them to effectively manage their health.

[0893] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0894] Step 1:

[0895] The user accesses a dedicated interface using a terminal and inputs information such as their age, health condition, and lifestyle habits. The input data is then sent from the terminal to the server. Examples of input data include "age: 35, health condition: diabetes, lifestyle habits: lack of exercise."

[0896] Step 2:

[0897] The server receives the user data sent from the terminal and stores it in a database. It uses a database management system (e.g., MySQL) to store the data in an appropriate format. The input data is stored in JSON format.

[0898] Step 3:

[0899] The server runs programs to analyze the stored user data. It uses programming languages ​​such as Python to analyze the data based on the user's age, health status, and lifestyle. For example, an algorithm might be run to suggest low-carb meals to a diabetic user.

[0900] Step 4:

[0901] The server generates an individually optimized meal plan based on the results of the data analysis. The meal plan is best suited to the user's health condition and lifestyle. For example, it might suggest a menu such as "Breakfast: low-carb bread, Lunch: salad chicken, Dinner: grilled fish."

[0902] Step 5:

[0903] The server sends the generated meal plan to the user's terminal, where the user can check the proposed meal plan through the terminal interface, and a notification function is used to notify the user that a new meal plan has been proposed.

[0904] Step 6:

[0905] The user can review the proposed meal plan and provide feedback if necessary. This feedback will be reflected in the next data analysis, leading to more accurate suggestions. For example, users can provide feedback such as, "I don't like this menu."

[0906] In this way, users can receive optimal meal plans based on their own health condition and lifestyle habits, enabling them to effectively manage their health.

[0907] Example 2

[0908] 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."

[0909] Conventional timing therapy requires clinic visits, which places a significant burden on users in terms of time and money. Furthermore, if the ovulation date is not accurately predicted, the effectiveness of timing therapy decreases. Furthermore, it is difficult to distinguish genes to improve the implantation rate in in vitro fertilization and intracytoplasmic sperm injection. To solve these problems, a more accurate and efficient timing therapy system is needed.

[0910] 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.

[0911] In this invention, the server includes a means for inputting male and female data and obtaining an optimal solution for timing therapy, a means for eliminating the need for outpatient visits for timing therapy, a means for identifying genes that increase the implantation rate of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICS), a means for collecting and preprocessing data on menstrual cycles and body temperature fluctuations to predict the day of ovulation, and a means for notifying the user of the prediction results. This allows users to accurately predict the day of ovulation and receive optimal timing therapy without visiting a hospital. Furthermore, genetic identification can improve the implantation rate of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICS).

[0912] "Data for men and women" includes information such as age, health status, and lifestyle habits.

[0913] The "optimal solution for timing therapy" refers to the optimal timing calculated based on data such as menstrual cycle and body temperature fluctuations.

[0914] The "means for eliminating the need for hospital visits for timing therapy" refers to a means that allows the user to perform timing therapy without visiting a medical institution.

[0915] "Methods for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc." refers to methods for identifying genes that improve implantation rates using gene sequencing technology.

[0916] "Data on menstrual cycle and body temperature fluctuations" refers to data that includes information such as the start date of a woman's menstruation and her basal body temperature.

[0917] "Preprocessing" refers to the process of cleaning the collected data, such as filling in missing values ​​and removing outliers.

[0918] The "means for predicting the date of ovulation" is a means for predicting the date of ovulation based on preprocessed data using a machine learning model or the like.

[0919] "Means for notifying the user of the prediction results" refers to sending information such as the predicted ovulation date to the user's device via push notification or email.

[0920] This invention is a system for obtaining an optimal solution for timing therapy, which inputs data on men and women, collects data on menstrual cycles and body temperature fluctuations, and implements an algorithm to predict the day of ovulation. A specific embodiment of this system will be described below.

[0921] System configuration

[0922] server:

[0923] The server plays a central role in collecting data, preprocessing, predicting ovulation days, and notifying results. Specifically, the following software and hardware are used:

[0924] Database: A relational database such as MySQL or PostgreSQL

[0925] Data preprocessing: Python's Pandas library

[0926] Machine learning models: Scikit-learn and TensorFlow

[0927] Notification systems: Firebase Cloud Messaging and SendGrid

[0928] Device:

[0929] A terminal is a device where a user inputs data and receives notifications from a server. Specifically, this applies to smartphones and PCs. Data is input and notifications are received using dedicated mobile or web applications.

[0930] User:

[0931] The user is responsible for inputting data on their menstrual cycle and body temperature fluctuations. Using a dedicated app, the user inputs data such as the start date of their menstrual period and basal body temperature.

[0932] System Operation

[0933] 1. Data Entry:

[0934] Users enter data about their menstrual cycle and body temperature fluctuations into a dedicated app. For example, a user opens the app on their smartphone and enters the start date of their period and their basal body temperature.

[0935] 2. Data Collection:

[0936] The device sends the data entered by the user to the server, and the app sends the data to the server via the Internet, and the server stores the received data in a database.

[0937] 3. Data Preprocessing:

[0938] The server preprocesses the collected data, specifically by imputing missing values ​​and removing outliers, and cleans the data using Python's Pandas library to standardize the preprocessed data.

[0939] 4. Ovulation Prediction:

[0940] The server runs an algorithm to predict ovulation based on the pre-processed data, using a machine learning model to predict ovulation and calculate when the next ovulation will occur.

[0941] 5. Result notification:

[0942] The server notifies the user of the calculated optimal timing by sending a push notification or email using Firebase Cloud Messaging or SendGrid, and the device receives the notification from the server and displays it to the user.

[0943] Specific examples

[0944] User A enters data on her menstrual cycle and body temperature fluctuations into a dedicated app. For example, User A opens the app and enters the start date of her period as "May 1, 2023" and her basal body temperature as "36.5°C." The device sends this data to the server. The server stores the received data in a database, complements missing values, and removes outliers. Next, the server runs an algorithm to predict the date of ovulation based on the preprocessed data, calculating that the next ovulation date will be "May 15, 2023." Finally, the server sends this information to User A's smartphone via push notification, and the device displays the message, "The next ovulation date is May 15, 2023."

[0945] Prompt Sentence Examples

[0946] "Please predict the next ovulation date based on the user's menstrual cycle and temperature fluctuation data. Also generate a message to notify the user of the prediction result."

[0947] In this way, specific embodiments of the system are described to clarify the details of the invention.

[0948] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0949] Step 1:

[0950] Data Entry

[0951] Users enter data about their menstrual cycle and body temperature fluctuations into a dedicated app. Specifically, users open the app on their smartphone and enter the start date of their period and their basal body temperature.

[0952] Input: menstruation start date, basal body temperature

[0953] Output: Input data is saved to the device

[0954] Step 2:

[0955] Data collection

[0956] The device sends the data entered by the user to the server, and the app sends the data to the server via the Internet, and the server stores the received data in a database.

[0957] Input: User-entered menstruation start date and basal body temperature

[0958] Output: Data stored on the server

[0959] Step 3:

[0960] Data Preprocessing

[0961] The server preprocesses the collected data, specifically by imputing missing values ​​and removing outliers, and cleans the data using Python's Pandas library to standardize the preprocessed data.

[0962] Input: Data stored on the server

[0963] Output: Preprocessed data

[0964] Step 4:

[0965] Ovulation prediction

[0966] The server runs an algorithm to predict ovulation based on the pre-processed data, using a machine learning model to predict ovulation and calculate when the next ovulation will occur.

[0967] Input: Preprocessed data

[0968] Output: Predicted ovulation date

[0969] Step 5:

[0970] Result notification

[0971] The server notifies the user of the calculated optimal timing by sending a push notification or email using Firebase Cloud Messaging or SendGrid, and the device receives the notification from the server and displays it to the user.

[0972] Input: Predicted ovulation date

[0973] Output: Notification displayed on the user's device

[0974] In this way, each processing step is specifically explained and the operation of the system is made clear.

[0975] (Application example 2)

[0976] 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."

[0977] Conventional timing therapy and in vitro fertilization / microinsemination require patients to visit a hospital, which places a significant burden on the user in terms of time and money. It is also difficult to accurately predict the day of ovulation, making it difficult to find the optimal timing. Furthermore, health data is recorded and analyzed manually, which makes it difficult to provide real-time notifications.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for hospital visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization, intracytoplasmic sperm injection, etc., means for predicting the date of ovulation from information such as the menstrual cycle and body temperature fluctuations and notifying the user of the optimal timing, and means for recording and analyzing the user's health data using a smartphone or smart glasses and notifying the user in real time. This allows the user to receive an accurate prediction of the date of ovulation and notification of the optimal timing without having to visit a hospital.

[0979] "Data on men and women" refers to data including information on age, health status, lifestyle habits, etc.

[0980] The "optimal solution for timing therapy" is a solution that calculates the optimal ovulation date and timing of fertilization based on information such as the menstrual cycle and body temperature fluctuations.

[0981] The "means for eliminating the need for hospital visits for timing therapy" refers to a means that allows a user to perform timing therapy without visiting a medical institution.

[0982] "Methods for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc." refers to the use of gene sequencing technology to identify specific genes that improve implantation rates.

[0983] "A means for predicting the date of ovulation from information such as menstrual cycle and body temperature fluctuations, and notifying the user of the optimal timing" is a means for analyzing the user's menstrual cycle and body temperature data, predicting the date of ovulation, and notifying the user of that information.

[0984] "Means for recording and analyzing a user's health data using a smartphone or smart glasses and notifying them in real time" refers to means for using a smartphone or smart glasses to collect and analyze a user's health data and notify the user of the results in real time.

[0985] A system for implementing the present invention is configured using hardware such as a server, a smartphone, smart glasses, etc. Specific embodiments are described below.

[0986] The server inputs data on men and women and provides a means to find the optimal timing therapy solution. This data includes information on age, health status, lifestyle habits, etc. The server analyzes this data and implements an algorithm that predicts the day of ovulation based on information such as menstrual cycle and temperature fluctuations. This algorithm is implemented using a programming language such as Python.

[0987] Users use their smartphones or smart glasses to record their health data, including the start date of their menstrual period and body temperature. This data is sent in real time to a server, which analyzes the data and calculates the optimal timing. The calculated results are then sent to the user via their smartphone or smart glasses.

[0988] As a specific example, consider a scenario in which a user uses a smartphone app to input the start date of their menstrual period, and the app predicts and notifies them of the next ovulation date. For example, if the user inputs the start dates of their menstrual period as September 1, 2023, September 29, 2023, and October 27, 2023, the app will predict the next ovulation date as November 10, 2023 and notify them.

[0989] Here's an example prompt that uses a generative AI model to analyze user data and calculate the optimal timing:

[0990] "When a user enters the first date of their period, the app will predict and notify them of their next ovulation date. For example, if a user enters the first date of their period on September 1, 2023, September 29, 2023, or October 27, 2023, the app will predict and notify them of their next ovulation date as November 10, 2023."

[0991] In this way, users can receive accurate predictions of their ovulation date and notifications of the optimal timing without having to visit a doctor.

[0992] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0993] Step 1:

[0994] The user uses a smartphone or smart glasses to input data such as the start date of their menstrual period and their body temperature. The input data is collected by the smartphone or smart glasses application. The input data includes the start date of their menstrual period (e.g., September 1, 2023, September 29, 2023, October 27, 2023) and body temperature data. This data serves as the basis for understanding the user's health condition.

[0995] Step 2:

[0996] The device (smartphone or smart glasses) sends the collected data to a server. The data includes the user's menstrual start date and body temperature. The server receives this data and stores it in a database. The data is sent in real time via the Internet.

[0997] Step 3:

[0998] The server analyzes the received data. Specifically, it calculates the average length of the menstrual cycle and predicts the next ovulation date. For example, if the menstrual start dates are September 1, 2023, September 29, 2023, and October 27, 2023, the server calculates the interval between these dates to determine the average cycle. It then subtracts 14 days from the average cycle and predicts the next ovulation date.

[0999] Step 4:

[1000] The server calculates the optimal timing based on the predicted ovulation date. The calculated timing is prepared as data to be notified to the user. For example, if the next ovulation date is predicted to be November 10, 2023, the server generates a message to notify the user of this date.

[1001] Step 5:

[1002] The server sends the generated notification message to the device. The notification message includes information about the predicted ovulation date and the optimal timing. The device displays the received notification message to the user. The user can receive the notification in real time via their smartphone or smart glasses.

[1003] Step 6:

[1004] The user can take appropriate action based on the information provided. For example, the information can be used to help manage the user's health, such as timing therapy to coincide with the predicted ovulation date. This makes it possible to receive accurate ovulation date predictions and notifications of the optimal timing without having to visit a doctor.

[1005] Example 3

[1006] 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."

[1007] Conventional infertility treatments include timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI), but it has been difficult to find the optimal timing for these treatments and to identify genes that increase implantation rates. Furthermore, the need for outpatient visits places a significant burden on patients in terms of time and money. Furthermore, there has been a lack of optimal treatment proposals based on the results of genetic analysis, which often results in treatment effectiveness not being maximized.

[1008] 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.

[1009] In this invention, the server includes a means for a user to upload genetic information, a means for the server to receive and store the genetic information, a means for the server to analyze the genetic information, a means for the server to generate a prompt sentence based on the analysis results, a means for the server to send the prompt sentence to the generative AI model, a means for the generative AI model to generate a proposal, and a means for the server to provide the proposal to the user. This allows a user to upload genetic information from home, and the server analyzes the information and proposes the optimal treatment. This eliminates the need for hospital visits, reduces time and financial burdens, and maximizes the effectiveness of treatment.

[1010] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[1011] "Timing therapy" is a treatment that involves having sexual intercourse at the optimal time to increase the chances of pregnancy.

[1012] "In vitro fertilization" is an infertility treatment in which eggs and sperm are fertilized outside the woman's body, and the fertilized eggs are then returned to the uterus.

[1013] "Intracytoplasmic sperm injection" is an infertility treatment method that uses a microscope to inject sperm directly into an egg to achieve fertilization.

[1014] "Implantation rate" refers to the success rate of a fertilized egg implanting in the uterine lining.

[1015] "Gene sequencing technology" is a technology for analyzing the base sequence of DNA.

[1016] The "means for users to upload genetic information" refers to an interface that allows users to provide their own genetic information to the system.

[1017] "Means for the server to receive and store genetic information" refers to the function by which the server receives genetic information provided by the user and safely stores it.

[1018] The "means by which the server analyzes genetic information" refers to a combination of software and hardware that allows the server to analyze the stored genetic information.

[1019] "Means for the server to generate a prompt sentence based on the analysis results" refers to the function by which the server creates a prompt sentence to be input into the generative AI model based on the analysis results of genetic information.

[1020] "Means for the server to send a prompt sentence to the generative AI model" refers to a communication means for the server to send the generated prompt sentence to the generative AI model.

[1021] "Means by which the generative AI model generates suggestions" refers to the function by which the generative AI model suggests the optimal treatment for the user based on a prompt sentence.

[1022] The "means by which the server provides suggestions to the user" is an interface by which the server notifies and provides suggestions from the generative AI model to the user.

[1023] This invention is a system that analyzes genetic information provided by a user, identifies genes that have the potential to increase implantation rates, and proposes optimal treatment methods. Specific embodiments of this system are described below.

[1024] First, users upload their genetic information through a dedicated web interface. This web interface is designed so that users can simply select a file and click the upload button. Once the user uploads their genetic information, the server receives the data and stores it in a secure database. When saving, a checksum is calculated to verify the integrity of the data, confirming that the data was saved correctly.

[1025] The server then uses gene sequencing software, such as Illumina's BaseSpace Sequence Hub, to analyze the stored genetic information. The server adds the analysis job to a queue and waits for the analysis to complete. Once the analysis is complete, the results are retrieved.

[1026] Based on the analysis results, the server generates a prompt. For example, if the analysis results indicate that a particular gene may increase the implantation rate, the server generates the following prompt:

[1027] "Based on the user's genetic information, we have determined that certain genes may increase implantation rates. Based on this information, we would like to suggest the most appropriate treatment for the user."

[1028] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-4). The transmission is done via an API. Based on the received prompt, the generative AI model generates a sentence proposing the optimal treatment for the user. For example, it might generate a suggestion such as, "According to the analysis results, a specific gene may increase the implantation rate. Therefore, we recommend that you consider treatment targeting this gene when undergoing in vitro fertilization or intracytoplasmic sperm injection."

[1029] Finally, the server provides the suggestions received from the generative AI model to the user, who can review the suggestions through a web interface. The server can also send email or push notifications to notify the user of the suggestions.

[1030] This system allows users to upload genetic information from home, and the server analyzes the information to suggest optimal treatments. This eliminates the need for hospital visits, reduces the time and financial burden, and maximizes the effectiveness of treatment. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[1031] Step 1:

[1032] Users upload genetic information.

[1033] Users upload genetic information through a dedicated web interface. Specifically, users open a web browser, access the system's web interface, and log in by entering their username and password on the login screen. They then go to the genetic information upload page, click the "Select File" button, select a genetic information file from their local computer, and click the "Upload" button. The input is the genetic information file, and the output is the file being sent to the server.

[1034] Step 2:

[1035] The server receives and stores the genetic information.

[1036] The server receives the genetic information uploaded by the user. The received data is stored in a secure database. When saving, a checksum is calculated to verify the integrity of the data, and it is confirmed that the data was saved correctly. The input is the genetic information file sent by the user, and the output is the genetic information stored in the database.

[1037] Step 3:

[1038] The server analyzes the genetic information.

[1039] The server uses gene sequencing software to analyze the stored genetic information. Specifically, the server utilizes gene sequencing technologies such as Illumina's BaseSpace Sequence Hub. The server adds analysis jobs to a queue and waits until the analysis is complete. Once the analysis is complete, the server retrieves the results. The input is the genetic information stored in the database, and the output is the analysis results.

[1040] Step 4:

[1041] The server generates a prompt based on the analysis results.

[1042] The server generates a prompt sentence based on the analysis results. For example, if the analysis results indicate that a specific gene may increase the implantation rate, the server generates a prompt sentence such as, "Based on the user's genetic information, it has been determined that a specific gene may increase the implantation rate. Based on this information, please suggest the optimal treatment for the user." The input is the analysis results, and the output is the generated prompt sentence.

[1043] Step 5:

[1044] The server sends a prompt to the generative AI model.

[1045] The server sends the generated prompt sentence to the generative AI model. The transmission is done through an API. The server waits for a response from the generative AI model. The input is the generated prompt sentence, and the output is the transmission of the prompt sentence to the generative AI model.

[1046] Step 6:

[1047] A generative AI model generates suggestions.

[1048] Based on the received prompt, the generative AI model generates a sentence proposing the optimal treatment to the user. For example, it may generate a suggestion such as, "According to the analysis results, a specific gene may increase the implantation rate. Therefore, we recommend that you consider treatment targeting this gene when undergoing in vitro fertilization or intracytoplasmic sperm injection." The input is the prompt, and the output is the generated suggestion.

[1049] Step 7:

[1050] The server provides the suggestions to the user.

[1051] The server provides the suggestions received from the generative AI model to the user. The user can review the suggestions through a web interface. The server can also send email or push notifications to notify the user of the suggestions. The input is the generated suggestions, and the output is providing the suggestions to the user.

[1052] (Application example 3)

[1053] 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."

[1054] In conventional IVF and ICSI, genetic testing was performed to increase implantation rates, but there was a lack of health risk assessment and preventative measures. Furthermore, there were also insufficient methods for finding optimal timing therapy or for eliminating the need for hospital visits. This made it difficult for users to comprehensively understand their own health status and take appropriate treatments and preventative measures.

[1055] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for inputting data on men and women and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization, intracytoplasmic sperm injection, etc., means for analyzing genetic information and assessing health risks, and means for generating a health risk report. This enables users to assess their health risks based on their own genetic information and take appropriate treatments or preventive measures.

[1056] "Gender data" refers to data about men and women, including information on age, health status, lifestyle habits, etc.

[1057] The "optimal solution for timing therapy" is a solution for administering treatment at the most effective timing based on data for men and women.

[1058] "Means for eliminating the need for hospital visits for timing therapy" refers to means for enabling a user to perform timing therapy at home without visiting a hospital.

[1059] "Genes that increase implantation rates" are genes that have the potential to improve the implantation rate of embryos in in vitro fertilization and intracytoplasmic sperm injection.

[1060] "Means for analyzing genetic information" refers to means for analyzing a DNA sample provided by a user and obtaining genetic data.

[1061] "Means for assessing health risks" refers to means for assessing the health risks of users based on the acquired genetic data.

[1062] The "means for generating a health risk report" refers to a means for generating a health risk report for a user based on the health risk assessment results.

[1063] The system for implementing this invention is composed of three main components: a server, a terminal, and a user. The role and specific processing of each component will be explained below.

[1064] server

[1065] The server plays a central role in analyzing genetic information, assessing health risks, and generating health risk reports. The following software is installed on the server:

[1066] Gene Sequencing Library (GeneSequencer): Analyzes DNA samples provided by users to obtain genetic data.

[1067] Health Risk Analysis Library (HealthRiskAnalyzer): Evaluates the user's health risk based on the acquired genetic data.

[1068] The server receives the user's DNA sample and analyzes the genetic information using a gene sequencing library. Next, it evaluates the health risk from the analyzed genetic information using a health risk analysis library. Finally, it generates a health risk report based on the evaluation results and provides it to the user.

[1069] Terminal

[1070] The terminal provides an interface for users to provide genetic information and receive health risk reports. The terminal includes the following features:

[1071] Uploading DNA samples: Users upload their own DNA samples to the server via their devices.

[1072] Display Health Risk Report: Displays the health risk report sent from the server to the user.

[1073] User

[1074] Users provide their DNA sample through the terminal and receive a health risk report. Users use the system by following the steps below:

[1075] 1. Use the terminal to upload your DNA sample to the server.

[1076] 2. The server analyzes the genetic information and assesses health risks.

[1077] 3. The health risk report generated by the server is received and displayed on the terminal.

[1078] Specific examples

[1079] For example, when a user uploads their DNA sample using a device, the server analyzes the genetic information using a gene sequencing library. Then, using a health risk analysis library, the server evaluates the risk of certain diseases (e.g., diabetes, heart disease, cancer, etc.) based on the analyzed genetic information. Finally, the server generates a health risk report and sends it to the user's device. The user can view this report through their device and receive appropriate preventive measures and suggestions for improving their lifestyle habits.

[1080] Prompt Sentence Examples

[1081] "User uploads DNA sample. Use genetic sequencing technology to identify genes with specific health risks and generate a health risk report."

[1082] The above is an embodiment of the present invention.

[1083] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1084] Step 1:

[1085] A user uploads a DNA sample using a terminal. The user provides their own DNA sample as input data through the terminal interface. The terminal sends this DNA sample to a server. The input is the DNA sample data, and the output is the data sent to the server.

[1086] Step 2:

[1087] The server analyzes the DNA sample using a gene sequencing library (GeneSequencer). The server uses the received DNA sample as input data and obtains genetic information using gene sequencing technology. The input is DNA sample data, and the output is analyzed genetic information.

[1088] Step 3:

[1089] The server analyzes genetic information using the health risk analysis library (HealthRiskAnalyzer) and evaluates health risks. The server uses the acquired genetic information as input data and performs data processing and calculations to evaluate health risks. The input is genetic information, and the output is the health risk assessment results.

[1090] Step 4:

[1091] The server generates a health risk report. The server uses the health risk assessment results as input data to generate a health risk report for the user. The input is the health risk assessment results, and the output is the health risk report.

[1092] Step 5:

[1093] The server sends the generated health risk report to the terminal. The server sends the generated health risk report as input data to the user's terminal. The input is the health risk report, and the output is data sent to the terminal.

[1094] Step 6:

[1095] The terminal displays the health risk report to the user. The terminal uses the health risk report received from the server as input data and displays it to the user. The input is the health risk report, and the output is the display to the user.

[1096] 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.

[1097] "Example 1"

[1098] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system includes an emotion engine that analyzes emotions from the user's voice, facial expressions, behavioral patterns, etc. Specifically, the system recognizes basic emotions such as joy, anger, sadness, and surprise from the voice of the user when they speak to the system. The system also captures the user's facial expressions with a camera or other sensor and reads emotions from subtle changes in facial expressions. Furthermore, the system infers emotions from the user's behavioral patterns, such as smartphone operation patterns and daily rhythms.

[1099] "Example 2"

[1100] The analysis results of the emotion engine are fed back to the means for obtaining the optimal solution for timing therapy. Specifically, when the user's emotional state is good, that is, when joy or a sense of relief is high, timing therapy visits are recommended. Conversely, when the user's emotional state is unstable, that is, when anger or sadness is high, a suggestion is made to refrain from timing therapy visits. This makes it possible to provide the optimal timing therapy according to the user's emotional state.

[1101] "Example 3"

[1102] The emotion engine also works with methods to identify genes that increase implantation rates in IVF and ICSI. Specifically, when the user's emotional state is good, it suggests the timing of IVF and ICSI. This makes it possible to proactively proceed with treatment when the user's emotional state may have a positive effect on implantation rates.

[1103] The processing flow of each embodiment will be described below.

[1104] "Example 1"

[1105] Step 1: The user speaks to the system, and the system records the user's voice.

[1106] Step 2: The recorded audio is sent to the emotion engine, which recognizes basic emotions from the audio.

[1107] Step 3: At the same time, the user's facial expressions are captured by a camera or other sensor, and emotions are read from subtle changes in facial expressions.

[1108] Step 4: Furthermore, emotions are inferred from the user's behavioral patterns, such as smartphone operation patterns and daily routines.

[1109] "Example 2"

[1110] Step 1: The analysis results of the emotion engine are fed back to the means for obtaining the optimal solution for timing therapy.

[1111] Step 2: When the user's emotional state is good, i.e., when feelings of joy and relief are high, recommend a timing therapy visit.

[1112] Step 3: Conversely, when the user's emotional state is unstable, i.e., when anger or sadness is high, it is suggested to refrain from visiting the timing therapy clinic.

[1113] "Example 3"

[1114] Step 1: The emotion engine will also work with methods to identify genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection.

[1115] Step 2: When the user's emotional state is good, the timing of IVF / ICI is suggested.

[1116] Example 1

[1117] 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."

[1118] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit a hospital, placing a significant burden on them. Furthermore, while it is known that emotional changes affect the effectiveness of treatment, there has been a lack of means to appropriately analyze this and provide feedback. Furthermore, there is a need for a system that can efficiently collect and analyze information such as the user's age, health condition, and lifestyle.

[1119] 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.

[1120] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc., means for analyzing emotions from the user's voice, facial expressions, behavioral patterns, etc., means for registering information such as the user's age, health condition, and lifestyle in a database, means for converting voice data to text using voice recognition software, means for analyzing facial expression data using face recognition software, means for collecting and analyzing the user's behavioral patterns, and means for feeding back the results of the emotion analysis to the user. This reduces the burden of outpatient visits and enables emotion analysis and feedback to improve the effectiveness of treatment.

[1121] "Gender data" refers to information such as the user's age, health condition, and lifestyle.

[1122] "Timing therapy" refers to a treatment that aims to find the optimal timing for reproduction in men and women.

[1123] "Means that eliminate the need for hospital visits" refers to means by which users can receive the necessary information and treatment through the system without having to visit a medical institution.

[1124] "In vitro fertilization / microinjection" refers to a reproductive technique in which an egg and sperm are fertilized outside the body and the fertilized egg is then returned to the uterus.

[1125] The "means for identifying genes that increase the implantation rate" refers to a means for identifying genes that increase the implantation rate of fertilized eggs using gene sequencing technology.

[1126] "Means for analyzing emotions" refers to technology for analyzing emotions from the user's voice, facial expressions, behavioral patterns, etc.

[1127] "Means for registering in a database" refers to technology for storing information such as a user's age, health condition, lifestyle habits, etc. in a database.

[1128] "Speech recognition software" means software for converting voice data into text.

[1129] "Facial recognition software" refers to software used to analyze facial expression data captured by a camera.

[1130] "Means for collecting and analyzing behavioral patterns" refers to technology for collecting and analyzing data such as users' smartphone operation patterns and lifestyle rhythms.

[1131] "Means for providing feedback on emotion analysis results" refers to technology for notifying users of the results of the analyzed emotions and providing appropriate advice and information.

[1132] MODE FOR CARRYING OUT THE INVENTION

[1133] This invention is a system that collects information on the user's age, health condition, lifestyle, etc., and performs emotion analysis to provide optimal solutions for timing therapy, in vitro fertilization, and intracytoplasmic sperm injection. Specific embodiments of this system are described below.

[1134] Entering user information and registering

[1135] Users enter information such as their age, health status, and lifestyle habits through a dedicated interface. This interface is provided as a web browser or smartphone app. For example, a user opens a smartphone app and enters their age and health status. The device then sends the information entered by the user to a server. The server then registers the received data in a relational database management system (RDBMS) such as MySQL or PostgreSQL.

[1136] Audio data collection and analysis

[1137] When a user speaks to the system, the device collects voice data, which the server converts into text using voice recognition software such as Google Cloud Speech-to-Text API or IBM Watson Speech to Text. For example, if a user says, "I'm very tired today," the speech is converted into text.

[1138] Facial expression data collection and analysis

[1139] The device uses a camera to capture the user's facial expression, and the server uses facial recognition software such as OpenCV or Microsoft Azure Face API to analyze subtle changes in facial expression. For example, if the user looks tired, that expression will be captured by the camera and analyzed.

[1140] Collecting and analyzing behavioral patterns

[1141] The device collects data such as the user's smartphone operation patterns and daily rhythm. The server analyzes this data to understand the user's behavioral patterns. For example, if a user uses their smartphone late into the night every night, that data is collected and analyzed.

[1142] Comprehensive analysis and feedback of emotions

[1143] The server comprehensively analyzes voice data, facial expression data, and behavioral pattern data to infer the user's emotions. The analysis results are fed back to the user. For example, if a user says, "I'm very tired today," and has a tired expression, the server will recognize the emotion of "fatigue" and provide feedback to the user saying, "Take it easy and rest today."

[1144] Examples and prompts

[1145] For example, a user opens a smartphone app and enters their age as "30," their health condition as "good," and their lifestyle as "I exercise every day." If the user then says, "I'm very tired today," the voice is converted into text, their facial expressions are captured by a camera, and their behavioral patterns are collected. The server analyzes this data, recognizes the emotion of "fatigue," and provides feedback to the user, saying, "Take it easy and rest today."

[1146] Examples of prompts to input to a generative AI model might include:

[1147] If a user says, "I feel very tired today," the speech recognition software converts this speech into text and the emotion engine recognizes it as "tired." If the user's facial expression looks tired, the facial recognition software detects this and the emotion engine reinforces the emotion of "tired."

[1148] In this way, the system can comprehensively grasp the user's state based on the user's input data and emotion analysis results.

[1149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1150] Program processing flow

[1151] Step 1: Enter your user information

[1152] Users enter information such as their age, health condition, lifestyle habits, etc. through a dedicated interface. For example, a user opens a smartphone app and enters their age as "30," their health condition as "good," and their lifestyle habits as "I exercise every day."

[1153] Input: information such as age, health condition, lifestyle habits, etc.

[1154] Output: User information entered

[1155] Step 2: Submit and register data

[1156] The terminal sends the information entered by the user to the server, which then registers the received data in a relational database management system (RDBMS) such as MySQL or PostgreSQL.

[1157] Input: User information

[1158] Output: User information registered in the database

[1159] Step 3: Collecting audio data

[1160] The device collects voice data when the user speaks to the system, for example, when the user says, "I'm very tired today."

[1161] Input: User's voice

[1162] Output: Collected audio data

[1163] Step 4: Analyzing the audio data

[1164] The server converts the collected voice data into text using voice recognition software such as Google Cloud Speech-to-Text API or IBM Watson Speech to Text.

[1165] Input: Audio data

[1166] Output: Text data

[1167] Step 5: Collect facial expression data

[1168] The device uses a camera to capture the user's facial expression. For example, if the user looks tired, the camera will capture that expression.

[1169] Input: User's facial expression

[1170] Output: Collected facial expression data

[1171] Step 6: Analyze facial expression data

[1172] The server uses facial recognition software such as OpenCV and Microsoft Azure Face API to analyze subtle changes in facial expressions.

[1173] Input: facial expression data

[1174] Output: Analyzed facial expression data

[1175] Step 7: Collecting behavioral patterns

[1176] The device collects data on the user's smartphone operation patterns, lifestyle rhythms, etc. For example, if the user operates the smartphone late into the night every night, that data is collected.

[1177] Input: Data on smartphone operation patterns, daily rhythms, etc.

[1178] Output: Collected behavioral pattern data

[1179] Step 8: Analyze behavioral patterns

[1180] The server analyzes the collected behavioral pattern data and understands the user's behavioral patterns.

[1181] Input: Behavioral pattern data

[1182] Output: Analyzed behavioral pattern data

[1183] Step 9: Comprehensive emotional analysis

[1184] The server comprehensively analyzes voice data, facial expression data, and behavioral pattern data to infer the user's emotions.

[1185] Input: Text data, analyzed facial expression data, analyzed behavior pattern data

[1186] Output: Inferred emotion data

[1187] Step 10: Feedback

[1188] The server then provides the analysis results as feedback to the user. For example, if a user says, "I'm very tired today," and has a tired expression, the server will recognize the emotion of "fatigue" and provide feedback to the user saying, "Take a good rest today."

[1189] Input: Inferred emotion data

[1190] Output: Feedback to the user

[1191] In this way, the system can comprehensively grasp the user's state based on the user's input data and emotion analysis results.

[1192] (Application example 1)

[1193] 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."

[1194] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit a hospital, placing a significant burden on them. Highly accurate technology was also required for genetic identification. Furthermore, there was no system capable of analyzing a user's emotional state in real time and detecting abnormal emotional states, which could result in delayed response in emergencies. To solve these issues, a system that comprehensively manages a user's health and emotional state and responds appropriately is needed.

[1195] 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.

[1196] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection, means for analyzing the user's emotional state and detecting abnormal emotional states, and means for sending a notification to an emergency contact if an abnormal emotional state is detected.This makes it possible to comprehensively manage the user's health and emotional state and take appropriate measures.

[1197] "Gender data" refers to data about men and women, including information on age, health status, lifestyle habits, etc.

[1198] "Timing therapy" is a therapy that maximizes the therapeutic effect by administering treatment at a specific timing.

[1199] An "optimal solution" is the most effective or efficient solution under given conditions and constraints.

[1200] "Measures that eliminate the need for hospital visits" are measures that allow patients to receive treatment or diagnosis remotely without having to visit a medical institution in person.

[1201] "In vitro fertilization / microinsemination" is a reproductive technique in which an egg and sperm are fertilized outside the body, and then the fertilized egg is returned to the uterus.

[1202] "Implantation rate" is the probability that a fertilized egg will implant in the uterine lining.

[1203] A "means for distinguishing genes" is a means for identifying specific genes using gene sequencing technology.

[1204] "Emotional state" refers to the psychological state of the user that is analyzed from their voice, facial expressions, behavioral patterns, etc.

[1205] An "abnormal emotional state" is an emotional state that is different from normal, such as extreme stress or fear.

[1206] "Emergency Contacts" are contacts who should be notified in the event of an emergency.

[1207] The system for carrying out the present invention can comprehensively manage the health and emotional state of a user and take appropriate measures. The system is configured using the following hardware and software.

[1208] Hardware

[1209] Smartphone: Equipped with a camera and microphone, it captures the user's voice and facial expressions.

[1210] Server: processes and stores data.

[1211] software

[1212] OpenCV: A library for detecting faces and analyzing facial expressions from camera footage.

[1213] Keras: A deep learning framework for implementing emotion recognition models.

[1214] SpeechRecognition: A library for analyzing user speech.

[1215] smtplib: A library for sending emails to emergency contacts when abnormal emotional states are detected.

[1216] Data processing and calculation

[1217] 1. Camera footage capture: Use your smartphone camera to capture footage in real time.

[1218] 2. Face detection and expression analysis: Detect faces using OpenCV and analyze facial expressions using Keras emotion recognition models.

[1219] 3. Speech Analysis: Analyze the user's speech using the SpeechRecognition library to infer their emotional state.

[1220] 4. Abnormal Emotion Detection: If an abnormal emotion such as fear is detected, send an email notification to emergency contacts using smtplib.

[1221] Specific examples

[1222] For example, if a user is walking alone late at night and suddenly feels scared, the system will detect that emotion and send an email to emergency contacts saying, "User is feeling scared."

[1223] Prompt Sentence Examples

[1224] "Your goal is to develop a security application that analyzes the user's emotional state in real time and sends a notification to emergency contacts if an abnormal emotional state is detected. Specifically, you will analyze emotions using the smartphone's camera and microphone, and implement a function that sends a notification by email if an abnormal emotion such as fear is detected."

[1225] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1226] Step 1:

[1227] Initialize your smartphone camera and capture video in real time.

[1228] Input: Smartphone camera image

[1229] Output: Captured real-time video

[1230] Specific operation: Activates the smartphone camera and captures video frame by frame.

[1231] Step 2:

[1232] Detects faces from captured video and analyzes facial expressions.

[1233] Input: Captured real-time video

[1234] Output: Detected face regions and expression data

[1235] Specific operation: Detects faces using OpenCV and analyzes facial expressions using Keras emotion recognition model.

[1236] Step 3:

[1237] It uses the smartphone's microphone to capture the user's voice and analyzes it.

[1238] Input: User's voice

[1239] Output: Analyzed audio data and emotional state

[1240] What it does: Converts speech to text using the SpeechRecognition library and infers emotional state.

[1241] Step 4:

[1242] The user's emotional state is comprehensively analyzed from facial expression data and voice data.

[1243] Input: facial expression data and voice data

[1244] Output: Overall emotional state

[1245] Specific operation: Integrates facial expression data and voice data, and analyzes the overall emotional state using an emotion engine.

[1246] Step 5:

[1247] If an abnormal emotional state (e.g., fear) is detected, a notification will be sent to emergency contacts.

[1248] Input: Overall emotional state

[1249] Output: Notification to emergency contacts

[1250] What it does: If an abnormal emotional state is detected, it uses smtplib to send an email to emergency contacts.

[1251] Step 6:

[1252] Provide feedback to the user that a notification has been sent.

[1253] Input: Notification result to emergency contact

[1254] Output: Feedback to the user

[1255] What it does: Displays a message to the user informing them that the notification was sent successfully.

[1256] Example 2

[1257] 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."

[1258] Conventional timing therapy systems have difficulty predicting the ovulation date based on data on the user's menstrual cycle and body temperature fluctuations, and have not been able to provide optimal timing therapy that takes the user's emotional state into consideration. Therefore, there is a demand for providing optimal timing therapy that takes the user's physical condition and emotional state into account.

[1259] 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.

[1260] In this invention, the server includes a means for analyzing data on menstrual cycle and body temperature fluctuations to predict the day of ovulation, a means for analyzing emotional states to adjust the optimal solution for timing therapy, and a means for notifying the user based on the analysis results, thereby making it possible to provide the optimal timing therapy based on the user's physical condition and emotional state.

[1261] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[1262] "Timing therapy" is a therapy that promotes pregnancy by having sexual intercourse at the optimal time based on the day of ovulation.

[1263] The "means of eliminating the need for hospital visits" refers to a means that allows the user to perform timing therapy at home without visiting a medical institution.

[1264] "In vitro fertilization / microinsemination" is an assisted reproductive technique in which an egg and sperm are fertilized outside the body and the fertilized egg is then transplanted into the uterus.

[1265] A "gene that increases implantation rate" is a gene that increases the probability that a fertilized egg will implant in the uterine lining.

[1266] A "menstrual cycle" is the period from the start of a woman's menstruation to the start of the next menstruation.

[1267] "Body temperature fluctuation" refers to the phenomenon in which a woman's basal body temperature fluctuates with her menstrual cycle.

[1268] "Ovulation day" is the day an egg is released from the ovary.

[1269] "Emotional state" indicates the user's psychological state, and includes joy, relief, anger, sadness, and the like.

[1270] "Analysis results" are the results obtained by the server analyzing the data.

[1271] "Notification" is a means of communicating information to the user about analysis results and feedback.

[1272] This invention is a system that allows users to input data on their menstrual cycle and body temperature fluctuations and provides optimal timing therapy taking into account their emotional state. The system is composed of three entities: a server, a terminal, and the user.

[1273] First, the user uses a device such as a smartphone or PC to input data about their menstrual cycle and body temperature fluctuations into a dedicated application. For example, there is a form for entering the start date of menstruation and basal body temperature. The device then sends the input data to a server in real time.

[1274] The server stores the received data in a database. This database uses a common database management system. When storing the data, the server checks the consistency of the data to ensure that there is no invalid data.

[1275] The server then runs an algorithm to predict ovulation based on the stored data. This algorithm is implemented in Python and uses libraries such as Pandas and NumPy to process and analyze the data. For example, it calculates the average and standard deviation of the menstrual cycle and predicts ovulation.

[1276] The server calculates the optimal timing based on the analysis results. Specifically, it calculates the optimal timing within a few days before and after the day of ovulation and sends this information in JSON format to the device.

[1277] The user selects their current emotional state using a dedicated application. For example, there are options such as "joy," "relief," "anger," and "sadness." The device then transmits the emotional data to the server.

[1278] The server analyzes the user's emotional state using an emotion engine, which is built using a generative AI model. For example, if the emotional state is "joy," it assigns a high numerical score.

[1279] The server adjusts the optimal timing of therapy based on the analysis results of the emotion engine. For example, if the emotion score is high, it generates a message recommending hospital visits. Conversely, if the emotion score is low, it generates a message suggesting refraining from hospital visits.

[1280] Finally, the server sends the optimal timing and feedback to the device. The device then provides the user with information via push notification or email. For example, a notification might say, "The next optimal timing is XX / XX. Your emotional state is good, so we recommend that you visit the hospital."

[1281] For example, if a user inputs data on their menstrual cycle and body temperature fluctuations, and their emotional state is set as "joy," the server analyzes this data and calculates the optimal timing. Since their emotional state is good, a notification is sent to the user's device recommending that they visit a clinic for timing therapy.

[1282] An example prompt is, "Please enter your menstrual cycle and body temperature fluctuation data. Also, please select your current emotional state. For example, joy, relief, anger, sadness, etc."

[1283] The system allows users to receive optimally timed therapy based on their physical and emotional state.

[1284] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1285] Step 1:

[1286] The user inputs data about their menstrual cycle and body temperature fluctuations into the terminal. Specifically, they input the menstrual start date and basal body temperature into a form in a dedicated application. The input data is sent from the terminal to the server. The input data includes the menstrual start date, basal body temperature, etc. The output is the user data sent to the server.

[1287] Step 2:

[1288] The server stores the received data in a database. Specifically, it stores the data using a database management system. When storing the data, it checks the integrity of the data to ensure that there is no invalid data. The input is the user data sent from the terminal, and the output is the data stored in the database.

[1289] Step 3:

[1290] The server runs an algorithm to predict ovulation based on the stored data. Specifically, it uses a script implemented in Python and libraries such as Pandas and NumPy to process and analyze the data. For example, it calculates the average and standard deviation of the menstrual cycle and predicts ovulation. The input is user data retrieved from the database, and the output is the predicted ovulation date.

[1291] Step 4:

[1292] The server calculates the optimal timing based on the analysis results. Specifically, it calculates the optimal timing for several days before and after the ovulation date and sends that information in JSON format to the device. The input is the predicted ovulation date, and the output is the optimal timing information.

[1293] Step 5:

[1294] The user selects their current emotional state using a dedicated application. Specifically, they select from options such as "joy," "relief," "anger," and "sadness." The input is the user's emotional state, and the output is emotional data sent from the device to the server.

[1295] Step 6:

[1296] The server uses an emotion engine to analyze the user's emotional state. Specifically, it uses a generative AI model to analyze the emotional data. For example, if the emotional state is "joy," it sets a high numerical score. The input is the emotional data sent from the device, and the output is the analyzed emotional score.

[1297] Step 7:

[1298] The server adjusts the optimal timing solution for therapy based on the analysis results of the emotion engine. Specifically, if the emotion score is high, a message recommending hospital visits is generated. Conversely, if the emotion score is low, a message suggesting refraining from hospital visits is generated. The input is the analyzed emotion score, and the output is the adjusted optimal solution message.

[1299] Step 8:

[1300] The server sends the optimal timing and feedback to the device. Specifically, the information is provided via push notification or email. For example, a notification such as "The next optimal timing is XX month XX day. Your emotional state is good, so we recommend that you visit the hospital." The input is the adjusted optimal solution message, and the output is the notification sent to the user.

[1301] (Application example 2)

[1302] 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."

[1303] In conventional infertility treatments such as timing therapy, in vitro fertilization, and intracytoplasmic sperm injection, the optimal timing is calculated based on data on both men and women, and genetic analysis is performed, but recommendations for clinic visits and predictions of ovulation dates are not sufficiently performed taking into account the user's emotional state. As a result, it was not possible to provide the optimal treatment timing according to the user's emotional state, and improving the effectiveness of treatment was an issue.

[1304] 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.

[1305] In this invention, the server includes means for inputting male and female data and obtaining the optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection, means for predicting the day of ovulation from information on menstrual cycle and body temperature fluctuations, and means for analyzing the user's emotional state and recommending outpatient visits when the user is in a good mood and suggesting that the user refrain from outpatient visits when the user is in an unstable mood. This makes it possible to provide optimal treatment timing according to the user's emotional state.

[1306] "Gender data" refers to information about individuals, such as age, health status, and lifestyle habits.

[1307] "Timing therapy" refers to a treatment that increases the chances of pregnancy by having sexual intercourse at the optimal time according to a woman's ovulation cycle.

[1308] "Measures to eliminate the need for hospital visits" refers to methods or technologies that allow users to receive treatment or diagnosis at home without having to visit a medical institution.

[1309] "In vitro fertilization / microinjection" refers to a type of infertility treatment in which an egg and sperm are fertilized outside the body, and then the fertilized egg is returned to the uterus.

[1310] "Genes that increase implantation rate" refer to genes that are associated with increasing the probability of a fertilized egg implanting in the uterine lining.

[1311] "Gene sequencing technology" refers to the technology for analyzing the base sequence of DNA.

[1312] The term "menstrual cycle" refers to the period from the start of a woman's menstruation to the start of the next menstruation.

[1313] "Body temperature fluctuation" refers to the change in an individual's body temperature depending on time and circumstances.

[1314] "Ovulation" refers to the day an egg is released from a woman's ovaries.

[1315] "Emotional state" refers to an individual's emotional state or mood.

[1316] "Analysis" refers to examining data or information in detail to clarify its meaning and structure.

[1317] "Recommending medical treatment" means recommending that the user visit a medical institution.

[1318] "Avoid medical visits" refers to encouraging users to avoid going to medical institutions.

[1319] The system for carrying out this invention consists of three main elements: a server, a terminal, and a user. The server includes means for inputting male and female data and obtaining the optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization and intracytoplasmic sperm injection, means for predicting the day of ovulation from information on menstrual cycle and body temperature fluctuations, and means for analyzing the user's emotional state, recommending outpatient visits when the user is emotionally well, and suggesting that the user refrain from outpatient visits when the user is emotionally unstable.

[1320] The server uses Python and its libraries (NumPy, scikit-learn) to analyze data and make predictions. Specifically, it obtains data on the user's menstrual cycle, body temperature fluctuations, and emotional state, and uses this data to predict ovulation. It also analyzes emotional state and makes recommendations for medical visits or suggestions for avoiding them.

[1321] The terminal is a device used by the user, such as a smartphone or tablet, through which data is input and results are received through an application installed on the device. The user inputs data such as menstrual cycle, body temperature, and emotional state into the application.

[1322] As an example, a user enters the following data into an application:

[1323] Menstrual cycle: [28, 30, 27, 29, 31]

[1324] Body temperature fluctuations: [36.5, 36.7, 36.8, 36.6, 36.9]

[1325] Emotional state: [7, 8, 6, 5, 9]

[1326] The server uses this data to predict the next ovulation date and decides whether to recommend a visit based on the patient's emotional state. An example of a prompt to be input to the generative AI model is as follows:

[1327] User's menstrual cycle data: [28, 30, 27, 29, 31]

[1328] User's temperature fluctuation data: [36.5, 36.7, 36.8, 36.6, 36.9]

[1329] User emotional state data: [7, 8, 6, 5, 9]

[1330] Use this data to predict your next ovulation date and decide whether to recommend a visit based on your emotional state.

[1331] This system allows users to easily input data at home and find the optimal timing for treatment. It also recommends clinic visits based on emotional state, reducing stress for users and improving treatment effectiveness.

[1332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1333] Step 1:

[1334] Users input data about their menstrual cycle, body temperature fluctuations, and emotional state into a smartphone application.

[1335] Input: Menstrual cycle data, body temperature fluctuation data, emotional state data

[1336] Output: User data is saved in the application

[1337] Step 2:

[1338] The terminal transmits the input data to the server.

[1339] Input: User data

[1340] Output: User data sent to the server

[1341] Step 3:

[1342] The server analyzes the data received and predicts the date of ovulation based on menstrual cycle and body temperature fluctuation data.

[1343] Input: Menstrual cycle data, body temperature fluctuation data

[1344] Output: Predicted ovulation date

[1345] How it works: The server uses Python's NumPy and scikit-learn libraries to build a linear regression model and predict the next ovulation date.

[1346] Step 4:

[1347] The server analyzes the emotional state data and recommends visiting the hospital when the emotions are good, and suggests refraining from visiting the hospital when the emotions are unstable.

[1348] Input: Emotional state data

[1349] Output: Recommendation to visit the hospital or suggestion to refrain from visiting the hospital

[1350] Specific operation: The server calculates the average emotional state, and if it exceeds a certain threshold, it recommends visiting the hospital, and if it is below the threshold, it suggests refraining from visiting the hospital.

[1351] Step 5:

[1352] The server sends the predicted ovulation date and recommendations for visiting the clinic to the device.

[1353] Input: Predicted ovulation date, recommended visit or avoidance of visit

[1354] Output: Result data sent to the terminal

[1355] Step 6:

[1356] The terminal notifies the user of the result data received from the server.

[1357] Input: Result data

[1358] Output: Predicted ovulation date and doctor visit suggestions displayed to the user

[1359] Specific operation: The device notifies the user through the application of the predicted ovulation date and suggestions for visiting the clinic.

[1360] This series of processes allows users to easily input data at home and find the optimal timing for treatment. In addition, by recommending hospital visits based on emotional state, it is possible to reduce user stress and improve treatment effectiveness.

[1361] Example 3

[1362] 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."

[1363] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit a clinic, placing a significant time and financial burden on the user. Furthermore, genetic screening to increase implantation rates has not been adequately performed, limiting the effectiveness of treatment. Furthermore, it is known that the user's emotional state affects the success of treatment, but no treatment timing suggestions have been made that take this into account. There is a need to resolve these issues and provide more effective and efficient infertility treatments.

[1364] 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.

[1365] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc., means for analyzing the user's genetic information and identifying genes that may increase implantation rates, and means for monitoring the user's emotional state and suggesting the timing of treatment when the user's emotional state is good. This allows the user to receive optimal treatment based on their genetic information while reducing the burden of outpatient visits, and also enables treatment to be carried out at a timing that takes their emotional state into consideration.

[1366] "Gender data" refers to information about men and women, such as age, health status, and lifestyle habits.

[1367] "Means for obtaining the optimal solution for timing therapy" refers to a method for calculating and proposing the optimal timing therapy based on data from men and women.

[1368] "Means for eliminating the need for hospital visits for timing therapy" refers to a method that allows a user to perform timing therapy at home without visiting a hospital.

[1369] "Means for identifying genes that increase implantation rates" refers to a method for identifying genes that may increase implantation rates using gene sequencing technology.

[1370] "Means for analyzing a user's genetic information" refers to a method for analyzing a genetic sample provided by a user and extracting genetic information.

[1371] "Means for identifying genes that may increase implantation rate" refers to a method for finding specific genes that may increase implantation rate from analyzed genetic information.

[1372] "Means for monitoring the user's emotional state" refers to a method for monitoring the user's emotional state in real time and collecting it as data.

[1373] The "means for suggesting the timing of treatment when the emotional state is good" refers to a method for suggesting the optimal timing of treatment when the emotional state of the user is determined to be good.

[1374] This invention is a system that proposes optimal fertility treatment methods and their timing through cooperation between users, servers, and terminals. Specific embodiments of this system are described below.

[1375] Hardware and software used

[1376] Hardware: High-performance servers, user devices (PCs, smartphones)

[1377] Software: Gene sequencing software, emotion engines, data analysis tools, generative AI models

[1378] System Overview

[1379] 1. Users provide genetic information

[1380] Users collect their own genetic samples using a dedicated genetic sample collection kit.

[1381] The user places the collected sample in a special container and sends it to the server using the specified method.

[1382] 2. The server analyzes the genetic information

[1383] The server analyzes the received genetic samples using genetic sequencing software (e.g., a common genetic sequencing tool).

[1384] The server processes the analysis results using data analysis tools (e.g., Python's Pandas library) to identify genes that may increase implantation rates.

[1385] 3. The server proposes a treatment method based on the analysis results

[1386] Based on the identified genetic information, the server suggests the most appropriate treatment for the user.

[1387] The server notifies the user terminal of the content of the proposal.

[1388] 4. The user confirms the proposed treatment.

[1389] The user receives a notification on their device and confirms the proposed treatment.

[1390] If necessary, the user will work with a medical institution to proceed with treatment.

[1391] 5. Emotion engine monitors the user's emotional state

[1392] The emotion engine monitors the user's emotional state in real time.

[1393] Users input their emotional state through a smartphone app.

[1394] 6. The server suggests the timing of treatment based on the patient's emotional state.

[1395] The server receives data from the emotion engine and, if it determines that the user's emotional state is good, suggests the timing of in vitro fertilization and intracytoplasmic sperm injection.

[1396] The server notifies the user terminal of the content of the proposal.

[1397] 7. The user proceeds with treatment based on the suggested timing

[1398] The user proceeds with treatment based on the suggested timing.

[1399] The user will work with the medical institution to schedule treatment.

[1400] Specific examples

[1401] Providing genetic information: The user uses a dedicated genetic sample collection kit and sends the sample to the server.

[1402] Notification of analysis results: Based on the analysis results, the server notifies the user's device, saying, "Your genetic information has identified genes that may increase the implantation rate. The optimal treatment is ____."

[1403] Emotional state monitoring: When a user types "I'm feeling good today" into a smartphone app, the emotion engine analyzes this and sends it to the server.

[1404] Timing suggestion: The server notifies the user, "Your current emotional state is good. It is the best time for IVF / ICI."

[1405] Example prompts for generative AI models

[1406] "How can we analyze the genetic information provided by users and identify genes that may increase implantation rates?"

[1407] "Please explain how the system works to suggest the best time for IVF / ICI when the user's emotional state is good."

[1408] In this way, a system is constructed in which the user, the server, and the terminal work together to propose the optimal treatment and timing. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1409] Step 1:

[1410] The user provides genetic information.

[1411] Input: User's genetic sample (e.g., saliva or blood)

[1412] Specific operation: The user uses a dedicated gene sample collection kit to collect their own gene sample, places the collected sample in a dedicated container, and sends it to the server using a specified method.

[1413] Output: Genetic sample sent to the server

[1414] Step 2:

[1415] The server analyzes the genetic information.

[1416] Input: Genetic sample submitted by the user

[1417] Specific operation: The server launches the gene sequencing software (e.g., a general gene sequencing tool), inputs the sample data, and begins analysis.

[1418] Data processing: Gene sequencing software analyzes the genetic information and generates genetic sequence data.

[1419] Output: Analyzed gene sequence data

[1420] Step 3:

[1421] The server will suggest a treatment method based on the analysis results.

[1422] Input: Analyzed gene sequence data

[1423] Specific operation: The server uses data analysis tools (e.g., Python's Pandas library) to process gene sequence data and identify genes that may increase implantation rates.

[1424] Data calculation: Data analysis tools analyze gene sequence data and extract specific genes.

[1425] Output: Genetic information that may increase implantation rate

[1426] Step 4:

[1427] The server notifies the user terminal of the proposed treatment.

[1428] Input: Genetic information that may increase implantation rate

[1429] Specific operation: The server generates a message saying, "Your genetic information has identified a gene that may increase the implantation rate. The optimal treatment is ____," and sends it to the user's device.

[1430] Output: Treatment proposal sent to the user's device

[1431] Step 5:

[1432] The user confirms the proposed treatment.

[1433] Input: Treatment proposal sent to user device

[1434] Specific operation: The user receives a notification on their device, confirms the proposed treatment, and, if necessary, works with a medical institution to proceed with the treatment.

[1435] Output: User's treatment selection and implementation

[1436] Step 6:

[1437] An emotion engine monitors the user's emotional state.

[1438] Input: User emotional state data

[1439] How it works: Users input their emotional state through a smartphone app, which the emotion engine analyzes and sends to the server.

[1440] Data processing: The emotion engine analyzes the user's emotional state data and evaluates the emotional state.

[1441] Output: Emotional state assessment data sent to the server

[1442] Step 7:

[1443] The server suggests timing of treatment based on emotional state.

[1444] Input: Emotional state assessment data

[1445] Specific operation: The server receives data from the emotion engine, and if it determines that the user's emotional state is good, it suggests the timing of IVF / ICI.

[1446] Data calculation: The server analyzes the emotional state assessment data and calculates the optimal timing for treatment.

[1447] Output: Treatment timing suggestions sent to the user device

[1448] Step 8:

[1449] The user proceeds with treatment based on the suggested timing.

[1450] Input: Treatment timing suggestions sent to the user's device

[1451] What happens: The user sees the notification and contacts their healthcare provider to schedule a treatment appointment.

[1452] Output: Confirm and execute the user's treatment schedule.

[1453] (Application example 3)

[1454] 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."

[1455] In conventional IVF and ICSI, genetic screening and timing optimization to increase implantation rates have not been adequately performed. Additionally, there is no system that comprehensively evaluates the user's health risks and emotional state and provides an optimal health management plan, making it difficult to maximize the effectiveness of treatment.

[1456] 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.

[1457] In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase implantation rates in in vitro fertilization and intracytoplasmic sperm injection, means for analyzing genetic information and identifying health risks, means for monitoring emotional states and evaluating stress levels, and means for comprehensively evaluating health and emotional states and providing an optimal health management plan. This makes it possible to comprehensively evaluate a user's genetic information and emotional state and provide the optimal treatment and health management plan.

[1458] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[1459] "Timing therapy" is a method of administering treatment at the optimal timing based on data for men and women.

[1460] "Methods that eliminate the need for hospital visits" are methods that allow users to receive treatment or diagnosis remotely without having to visit a medical institution.

[1461] The "means for identifying genes that increase implantation rates" refers to a means for identifying genes that have the potential to improve implantation rates in in vitro fertilization or intracytoplasmic sperm injection using gene sequencing technology.

[1462] "Means for analyzing genetic information and identifying health risks" refers to means for analyzing genetic information provided by users and identifying health risks such as heart disease and diabetes.

[1463] The "means for monitoring emotional state and assessing stress level" refers to a means for monitoring a user's emotional state in real time and assessing the level of stress or anxiety.

[1464] "Means for comprehensively evaluating health status and emotional state and providing an optimal health management plan" refers to a means for comprehensively evaluating a user's genetic information and emotional state and proposing an optimal health management plan.

[1465] A system for implementing this invention includes a server, a user terminal, and various sensor devices. The server analyzes genetic information provided by the user and identifies health risks. It also monitors the user's emotional state and evaluates their stress level. The system comprehensively evaluates this data and provides an optimal health management plan.

[1466] Hardware and software used

[1467] Hardware: smartphones, gene sequencing devices, emotional state monitoring sensors

[1468] Software: Python, gene sequencing analysis tool, emotional state monitoring tool

[1469] Data processing and calculation

[1470] 1. Genetic analysis:

[1471] The server receives the genetic information provided by the user and analyzes it using a gene sequencing analysis tool, identifying health risks such as heart disease and diabetes from the analysis results.

[1472] 2. Emotional state monitoring:

[1473] The user terminal collects the user's emotional state in real time using an emotional state monitoring sensor, and the collected data is sent to a server, where the stress level is evaluated using an emotional state monitoring tool.

[1474] 3. Providing Health Care Plans:

[1475] The server comprehensively evaluates the genetic information and emotional state data to generate an optimal health management plan, which is then sent to the user's smartphone.

[1476] Specific examples

[1477] For example, if a user uploads their genetic information to the app, the server will identify their risk of heart disease and diabetes. If the emotional state monitoring function assesses the user's stress level as "high," the server will notify the user, "Suggesting yoga or meditation to help you relax."

[1478] Prompt Sentence Examples

[1479] Analyze your users' genetic information to identify health risks, monitor their emotional state, assess their stress levels, and use this information to suggest optimal health management plans.

[1480] In this way, a specific embodiment for carrying out the invention has been shown, and it has been made clear how the user can use this system.

[1481] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1482] Step 1:

[1483] Users upload their genetic information to a smartphone app.

[1484] Input: User's genetic information file

[1485] Output: Genetic information is sent to the server

[1486] Specific operation: The user selects a genetic information file and presses the upload button in the app, which then sends the genetic information to the server.

[1487] Step 2:

[1488] The server receives the genetic information and performs analysis using a genetic sequencing analysis tool.

[1489] Input: Genetic information sent to the server

[1490] Output: Health risk identification results

[1491] How it works: The server inputs genetic information into an analysis tool to identify health risks such as heart disease and diabetes. The analysis results are stored in a database.

[1492] Step 3:

[1493] The user terminal collects the user's emotional state using an emotional state monitoring sensor.

[1494] Input: Real-time emotional state data of the user

[1495] Output: Emotional state data is sent to the server

[1496] Specific operation: The user terminal periodically collects data from the emotional state monitoring sensor and transmits it to the server.

[1497] Step 4:

[1498] A server receives the emotional state data and assesses stress levels using an emotional state monitoring tool.

[1499] Input: Emotional state data sent to the server

[1500] Output: Stress level evaluation result

[1501] Specific operation: The server inputs emotional state data into the monitoring tool and evaluates stress levels. The evaluation results are stored in a database.

[1502] Step 5:

[1503] The server comprehensively evaluates genetic information and emotional state data to generate an optimal health management plan.

[1504] Input: Health risk identification results, stress level assessment results

[1505] Output: Health management plan

[1506] How it works: The server integrates genetic information and emotional state data, and uses a generative AI model to generate an optimal health management plan. The plan is then stored in a database.

[1507] Step 6:

[1508] The server notifies the user's smartphone of the generated health management plan.

[1509] Input: Health Care Plan

[1510] Output: Health management plan displayed on the user's smartphone

[1511] Specific operation: The server sends the health management plan to the user's smartphone, and the app displays the plan.

[1512] 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.

[1513] 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.

[1514] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1515] 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.

[1516] [Third embodiment]

[1517] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1518] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1519] 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).

[1520] 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.

[1521] 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.

[1522] 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).

[1523] 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.

[1524] 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.

[1525] 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.

[1526] 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.

[1527] 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.

[1528] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1529] "Example 1"

[1530] One form of the present invention is a system that registers information such as age, health status, and lifestyle habits into a database as a means of inputting data on men and women. Specifically, users enter their own information through a dedicated interface, and the system collects and analyzes it.

[1531] "Example 2"

[1532] Additionally, as a means of finding the optimal solution for timing therapy, an algorithm will be implemented to calculate the optimal timing based on the collected data. Specifically, the system will predict the day of ovulation based on information such as a woman's menstrual cycle and body temperature fluctuations, and notify the user of the optimal timing based on that.

[1533] "Example 3"

[1534] Furthermore, gene sequencing technology is used as a means of identifying genes that increase implantation rates in IVF and ICSI. Specifically, the genetic information provided by the user is analyzed to identify genes that have the potential to increase implantation rates. This makes it possible to propose the most suitable treatment for each user.

[1535] The processing flow of each embodiment will be described below.

[1536] "Example 1"

[1537] Step 1: The user enters information such as their age, health condition, and lifestyle habits through a dedicated interface.

[1538] Step 2: The system registers the information provided by the user in a database.

[1539] Step 3: The system analyzes the registered information and suggests the most appropriate treatment for the user.

[1540] "Example 2"

[1541] Step 1: The system uses information provided by the user to predict the date of ovulation based on the woman's menstrual cycle, body temperature fluctuations, etc.

[1542] Step 2: The system calculates the optimal timing based on the predicted ovulation date.

[1543] Step 3: The system notifies the user of the optimal timing.

[1544] "Example 3"

[1545] Step 1: The system collects genetic information provided by the user.

[1546] Step 2: The genetic information collected by the system is analyzed using gene sequencing technology.

[1547] Step 3: The system uses the analysis results to identify genes that may increase implantation rates and suggests the most appropriate treatment for the user.

[1548] Example 1

[1549] 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."

[1550] Conventional timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI) require patients to visit the hospital, placing a significant burden on them both in terms of time and money. Genetic identification to increase implantation rates is also difficult, making it difficult to find the optimal treatment. Furthermore, there is a lack of efficient means to collect and analyze information such as the patient's age, health condition, and lifestyle, making personalized treatment difficult.

[1551] 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.

[1552] In this invention, the server includes: a means for the user to input information such as age, health condition, and lifestyle habits through a dedicated interface; a means for the terminal to send the input data to the server; a means for the server to register the data in a database; a means for the server to collect and analyze the data; a means for the server to input the analysis results into a generative AI model; and a means for the server to return the results of the generative AI model to the user. This allows patients to input their own information and find the optimal treatment without having to visit a hospital. It also enables efficient genetic identification and the provision of personalized treatments.

[1553] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[1554] "Timing therapy" is a therapy that maximizes the therapeutic effect by administering treatment at a specific timing.

[1555] "Measures that eliminate the need for hospital visits" are measures that allow patients to receive treatment or diagnosis remotely without having to visit a medical institution in person.

[1556] "In vitro fertilization / microinsemination" is an assisted reproductive technique in which sperm and eggs are fertilized outside the body, and the fertilized eggs are then transplanted into the uterus.

[1557] The "means for identifying genes that increase implantation rates" refers to means for identifying specific genes that increase implantation rates using gene sequencing technology.

[1558] A "dedicated interface" is a specific input screen or application designed for users to enter their information.

[1559] A "terminal" is a device (e.g., a smartphone or PC) that a user uses to input information and send it to a server.

[1560] "Server" means a computer system that receives, processes, stores, and analyzes data sent by users.

[1561] A "database" is a system for efficiently storing and managing collected data.

[1562] A "generative AI model" is an artificial intelligence model that generates analytical results based on input data.

[1563] A "prompt" is an instruction to be input into a generative AI model, and is a statement used to perform a specific analysis or generation.

[1564] This invention is a system that allows users to input information such as age, health status, and lifestyle habits through a dedicated interface, registers the information in a database, and collects and analyzes it.The system then inputs the analysis results into a generative AI model, and returns the generated results to the user, providing optimal treatment.

[1565] Users enter their information using a dedicated interface (e.g., a web application or a mobile application). This interface is built using web technologies such as HTML, CSS, and JavaScript. The information entered by users includes their age, health status, lifestyle habits, etc.

[1566] The terminal (user's device) sends the entered data to the server as an HTTP request. The data is sent in JSON format. The server analyzes the received data and registers it in a database. The server receives and registers the data using frameworks such as Python's Flask or Django, or Node.js's Express. MySQL or PostgreSQL is used as the database.

[1567] After the data is registered in the database, the server collects and analyzes the data using data analysis libraries such as Python's pandas and scikit-learn. For example, it can analyze trends in the age distribution and health status of users.

[1568] The server inputs the analysis results into a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. A prompt sentence is generated and input into the model to obtain detailed analysis results. Examples of prompt sentences include the following:

[1569] Prompt: "If a 30-year-old user exercises daily and is in good health, analyze what health trends they exhibit compared to other users of the same age."

[1570] The server returns the results obtained from the generative AI model to the user. The results are displayed on the user's device. For example, HTML and JavaScript are used to display the results on a web page. The user can view the results through the web page.

[1571] As a concrete example, consider a scenario in which a user uses a dedicated web interface to enter the following information:

[1572] Age: 30

[1573] Health: Good

[1574] Lifestyle: Exercise every day

[1575] This information is sent to a server and registered in a database. The registered data is collected by the server and used to analyze age distribution and health trends. The analysis results are input into a generative AI model, which generates detailed results. For example, a result may be that "30-year-old users who exercise daily have a high rate of good health compared to their peers." The results are displayed on the user's device, where they can check them.

[1576] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1577] Step 1:

[1578] The user enters information

[1579] Using a dedicated interface, users enter information such as their age, health status, and lifestyle habits. Specifically, they open a web browser, access a dedicated web page, enter the required information in a form, and click the "Submit" button. The input data includes age (e.g., 30 years old), health status (e.g., good), and lifestyle habits (e.g., exercise daily).

[1580] Step 2:

[1581] The device sends the input data to the server

[1582] The terminal converts the data entered by the user into JSON format and sends it to the server as an HTTP POST request. Specifically, it sends the following JSON data:

[1583] json{"age": 30,"health_status": "good","lifestyle": "exercise daily"}

[1584] The input is the data entered by the user, and the output is the JSON data sent to the server.

[1585] Step 3:

[1586] The server registers the data in the database

[1587] The server parses the received JSON data and registers it in a database. Specifically, it uses frameworks such as Python's Flask or Django, or Node.js's Express to receive and register the data. MySQL or PostgreSQL is used as the database. The following SQL query is executed.

[1588] sql

[1589] INSERT INTO user_data (age, health_status, lifestyle) VALUES (30, 'Good', 'Exercise daily');

[1590] The input is the JSON data sent to the server, and the output is the data registered in the database.

[1591] Step 4:

[1592] The server collects and analyzes the data

[1593] The server retrieves all user data from the database and analyzes trends in age distribution and health status. Specifically, it uses data analysis libraries such as Python's pandas and scikit-learn. The following Python code is used:

[1594] python

[1595] import pandas as pd

[1596] Retrieving data from a database

[1597] data = pd.read_sql('SELECT FROM user_data', connection)

[1598] Age distribution analysis

[1599] age_distribution = data['age'].value_counts()

[1600] Analyze health trends

[1601] health_trends = data['health_status'].value_counts()

[1602] The input is all user data obtained from the database, and the output is the analysis results of age distribution and health status trends.

[1603] Step 5:

[1604] The server inputs the analysis results into the generative AI model

[1605] The server generates a prompt based on the analysis results and inputs it into the generative AI model. Specifically, it generates the following prompt:

[1606] Prompt: "If a 30-year-old user exercises daily and is in good health, analyze what health trends they exhibit compared to other users of the same age."

[1607] The input is the analysis results of age distribution and health status trends, and the output is a prompt sentence to be input into the generative AI model.

[1608] Step 6:

[1609] The server returns the results of the generated AI model to the user

[1610] The server returns the results obtained from the generative AI model to the user. Specifically, it uses HTML and JavaScript to display the results obtained from the generative AI model on the user's web page. For example, it displays the results as follows:

[1611] The result: "30-year-old users who exercise daily are more likely to be in good health than their peers."

[1612] The input is the result obtained from the generative AI model, and the output is the result displayed on the user's device.

[1613] (Application example 1)

[1614] 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."

[1615] Conventional timing therapy, IVF, and ICSI require patients to visit the hospital, which places a significant burden on them. Furthermore, optimal dietary plans based on individual health conditions and lifestyle habits were not proposed, and health management was not adequately implemented. This resulted in the problem of not maximizing the effectiveness of treatment.

[1616] 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.

[1617] In this invention, the server includes a means for inputting male and female data and obtaining an optimal solution for timing therapy, a means for eliminating the need for outpatient visits for timing therapy, a means for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc., and a means for collecting information such as the user's age, health condition, and lifestyle habits and proposing an individually optimized diet plan. This reduces the burden of outpatient visits for patients and makes it possible to maximize the effectiveness of treatment by proposing an optimal diet plan based on the individual's health condition and lifestyle habits.

[1618] "Gender data" refers to information about individuals such as age, gender, health status, and lifestyle habits.

[1619] "Timing therapy" refers to a medical technique that maximizes effectiveness by administering treatment at a specific time.

[1620] An "optimal solution" refers to the most effective solution given the conditions and constraints.

[1621] "Non-visit measures" refer to methods that allow patients to receive treatment or diagnosis without visiting a medical institution in person.

[1622] "In vitro fertilization / microinjection" refers to an assisted reproductive technique in which an egg and sperm are fertilized outside the body and the fertilized egg is then returned to the uterus.

[1623] "Genes that increase implantation rate" refer to genes that increase the probability of a fertilized egg implanting in the uterine lining.

[1624] "Genetic discrimination means" refers to a method for identifying a specific gene and confirming its presence or characteristics.

[1625] "Information on age, health status, lifestyle habits, etc." refers to data such as an individual's age, current health status, and daily lifestyle habits.

[1626] "Individually optimized meal plans" refer to customized meal suggestions based on an individual's health and lifestyle.

[1627] "Means of collection and analysis" refers to the methods of collecting data and analyzing it to derive useful information.

[1628] A system for implementing this invention includes a server, a user terminal, and a database. The server runs a program for collecting and analyzing data from users. The user terminal is a device such as a smartphone or PC, and provides an interface for users to input their information. The database stores the collected data and allows the server to access it as needed.

[1629] The server executes a program that includes the following means:

[1630] 1. Data collection methods:

[1631] The system collects information such as age, health status, and lifestyle habits sent from the user's device. The user inputs this information through a dedicated interface. For example, the user inputs their age and health status using a smartphone app.

[1632] 2. Data analysis methods:

[1633] The server analyzes the collected data and generates an individually optimized meal plan using a programming language such as Python. For example, if the user has diabetes, it will suggest a low-carb menu.

[1634] 3. Proposed method:

[1635] Based on the analysis results, the app will suggest the optimal meal plan for the user. The suggestion will be sent to the user's device. For example, when the user opens the app, the recommended meal plan will be displayed.

[1636] The specific hardware and software configuration is as follows:

[1637] Hardware: smartphones, PCs, servers

[1638] Software: Python, database management system (e.g., MySQL), user interface library (e.g., Tkinter)

[1639] The server stores the data entered by the user in a database in JSON format and analyzes it as needed. The analysis results are sent to the user's device, and the user can receive an optimal meal plan based on their health condition and lifestyle.

[1640] As a concrete example, the following prompt sentence can be input to a generative AI model:

[1641] Example prompt sentence:

[1642] Develop an application that suggests optimal meal plans based on the user's age, health condition, and lifestyle habits. The information entered by the user includes name, age, health condition (e.g., diabetes, high blood pressure, etc.), and lifestyle habits (e.g., lack of exercise, smoking, etc.). Implement a function that collects and analyzes this information to suggest individually optimized menus.

[1643] In this way, users can receive optimal meal plans based on their own health condition and lifestyle habits, enabling them to effectively manage their health.

[1644] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1645] Step 1:

[1646] The user accesses a dedicated interface using a terminal and inputs information such as their age, health condition, and lifestyle habits. The input data is then sent from the terminal to the server. Examples of input data include "age: 35, health condition: diabetes, lifestyle habits: lack of exercise."

[1647] Step 2:

[1648] The server receives the user data sent from the terminal and stores it in a database. It uses a database management system (e.g., MySQL) to store the data in an appropriate format. The input data is stored in JSON format.

[1649] Step 3:

[1650] The server runs programs to analyze the stored user data. It uses programming languages ​​such as Python to analyze the data based on the user's age, health status, and lifestyle. For example, an algorithm might be run to suggest low-carb meals to a diabetic user.

[1651] Step 4:

[1652] The server generates an individually optimized meal plan based on the results of the data analysis. The meal plan is best suited to the user's health condition and lifestyle. For example, it might suggest a menu such as "Breakfast: low-carb bread, Lunch: salad chicken, Dinner: grilled fish."

[1653] Step 5:

[1654] The server sends the generated meal plan to the user's terminal, where the user can check the proposed meal plan through the terminal interface, and a notification function is used to notify the user that a new meal plan has been proposed.

[1655] Step 6:

[1656] The user can review the proposed meal plan and provide feedback if necessary. This feedback will be reflected in the next data analysis, leading to more accurate suggestions. For example, users can provide feedback such as, "I don't like this menu."

[1657] In this way, users can receive optimal meal plans based on their own health condition and lifestyle habits, enabling them to effectively manage their health.

[1658] Example 2

[1659] 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."

[1660] Conventional timing therapy requires clinic visits, which places a significant burden on users in terms of time and money. Furthermore, if the ovulation date is not accurately predicted, the effectiveness of timing therapy decreases. Furthermore, it is difficult to distinguish genes to improve the implantation rate in in vitro fertilization and intracytoplasmic sperm injection. To solve these problems, a more accurate and efficient timing therapy system is needed.

[1661] 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.

[1662] In this invention, the server includes a means for inputting male and female data and obtaining an optimal solution for timing therapy, a means for eliminating the need for outpatient visits for timing therapy, a means for identifying genes that increase the implantation rate of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICS), a means for collecting and preprocessing data on menstrual cycles and body temperature fluctuations to predict the day of ovulation, and a means for notifying the user of the prediction results. This allows users to accurately predict the day of ovulation and receive optimal timing therapy without visiting a hospital. Furthermore, genetic identification can improve the implantation rate of in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICS).

[1663] "Data for men and women" includes information such as age, health status, and lifestyle habits.

[1664] The "optimal solution for timing therapy" refers to the optimal timing calculated based on data such as menstrual cycle and body temperature fluctuations.

[1665] The "means for eliminating the need for hospital visits for timing therapy" refers to a means that allows the user to perform timing therapy without visiting a medical institution.

[1666] "Methods for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc." refers to methods for identifying genes that improve implantation rates using gene sequencing technology.

[1667] "Data on menstrual cycle and body temperature fluctuations" refers to data that includes information such as the start date of a woman's menstruation and her basal body temperature.

[1668] "Preprocessing" refers to the process of cleaning the collected data, such as filling in missing values ​​and removing outliers.

[1669] The "means for predicting the date of ovulation" is a means for predicting the date of ovulation based on preprocessed data using a machine learning model or the like.

[1670] "Means for notifying the user of the prediction results" refers to sending information such as the predicted ovulation date to the user's device via push notification or email.

[1671] This invention is a system for obtaining an optimal solution for timing therapy, which inputs data on men and women, collects data on menstrual cycles and body temperature fluctuations, and implements an algorithm to predict the day of ovulation. A specific embodiment of this system will be described below.

[1672] System configuration

[1673] server:

[1674] The server plays a central role in collecting data, preprocessing, predicting ovulation days, and notifying results. Specifically, the following software and hardware are used:

[1675] Database: A relational database such as MySQL or PostgreSQL

[1676] Data preprocessing: Python's Pandas library

[1677] Machine learning models: Scikit-learn and TensorFlow

[1678] Notification systems: Firebase Cloud Messaging and SendGrid

[1679] Device:

[1680] A terminal is a device where a user inputs data and receives notifications from a server. Specifically, this applies to smartphones and PCs. Data is input and notifications are received using dedicated mobile or web applications.

[1681] User:

[1682] The user is responsible for inputting data on their menstrual cycle and body temperature fluctuations. Using a dedicated app, the user inputs data such as the start date of their menstrual period and basal body temperature.

[1683] System Operation

[1684] 1. Data Entry:

[1685] Users enter data about their menstrual cycle and body temperature fluctuations into a dedicated app. For example, a user opens the app on their smartphone and enters the start date of their period and their basal body temperature.

[1686] 2. Data Collection:

[1687] The device sends the data entered by the user to the server, and the app sends the data to the server via the Internet, and the server stores the received data in a database.

[1688] 3. Data Preprocessing:

[1689] The server preprocesses the collected data, specifically by imputing missing values ​​and removing outliers, and cleans the data using Python's Pandas library to standardize the preprocessed data.

[1690] 4. Ovulation Prediction:

[1691] The server runs an algorithm to predict ovulation based on the pre-processed data, using a machine learning model to predict ovulation and calculate when the next ovulation will occur.

[1692] 5. Result notification:

[1693] The server notifies the user of the calculated optimal timing by sending a push notification or email using Firebase Cloud Messaging or SendGrid, and the device receives the notification from the server and displays it to the user.

[1694] Specific examples

[1695] User A enters data on her menstrual cycle and body temperature fluctuations into a dedicated app. For example, User A opens the app and enters the start date of her period as "May 1, 2023" and her basal body temperature as "36.5°C." The device sends this data to the server. The server stores the received data in a database, complements missing values, and removes outliers. Next, the server runs an algorithm to predict the date of ovulation based on the preprocessed data, calculating that the next ovulation date will be "May 15, 2023." Finally, the server sends this information to User A's smartphone via push notification, and the device displays the message, "The next ovulation date is May 15, 2023."

[1696] Prompt Sentence Examples

[1697] "Please predict the next ovulation date based on the user's menstrual cycle and temperature fluctuation data. Also generate a message to notify the user of the prediction result."

[1698] In this way, specific embodiments of the system are described to clarify the details of the invention.

[1699] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1700] Step 1:

[1701] Data Entry

[1702] Users enter data about their menstrual cycle and body temperature fluctuations into a dedicated app. Specifically, users open the app on their smartphone and enter the start date of their period and their basal body temperature.

[1703] Input: menstruation start date, basal body temperature

[1704] Output: Input data is saved to the device

[1705] Step 2:

[1706] Data collection

[1707] The device sends the data entered by the user to the server, and the app sends the data to the server via the Internet, and the server stores the received data in a database.

[1708] Input: User-entered menstruation start date and basal body temperature

[1709] Output: Data stored on the server

[1710] Step 3:

[1711] Data Preprocessing

[1712] The server preprocesses the collected data, specifically by imputing missing values ​​and removing outliers, and cleans the data using Python's Pandas library to standardize the preprocessed data.

[1713] Input: Data stored on the server

[1714] Output: Preprocessed data

[1715] Step 4:

[1716] Ovulation prediction

[1717] The server runs an algorithm to predict ovulation based on the pre-processed data, using a machine learning model to predict ovulation and calculate when the next ovulation will occur.

[1718] Input: Preprocessed data

[1719] Output: Predicted ovulation date

[1720] Step 5:

[1721] Result notification

[1722] The server notifies the user of the calculated optimal timing by sending a push notification or email using Firebase Cloud Messaging or SendGrid, and the device receives the notification from the server and displays it to the user.

[1723] Input: Predicted ovulation date

[1724] Output: Notification displayed on the user's device

[1725] In this way, each processing step is specifically explained and the operation of the system is made clear.

[1726] (Application example 2)

[1727] 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."

[1728] Conventional timing therapy and in vitro fertilization / microinsemination require patients to visit a hospital, which places a significant burden on the user in terms of time and money. It is also difficult to accurately predict the day of ovulation, making it difficult to find the optimal timing. Furthermore, health data is recorded and analyzed manually, which makes it difficult to provide real-time notifications.

[1729] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting male and female data and obtaining an optimal solution for timing therapy, means for eliminating the need for hospital visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization, intracytoplasmic sperm injection, etc., means for predicting the date of ovulation from information such as the menstrual cycle and body temperature fluctuations and notifying the user of the optimal timing, and means for recording and analyzing the user's health data using a smartphone or smart glasses and notifying the user in real time. This allows the user to receive an accurate prediction of the date of ovulation and notification of the optimal timing without having to visit a hospital.

[1730] "Data on men and women" refers to data including information on age, health status, lifestyle habits, etc.

[1731] The "optimal solution for timing therapy" is a solution that calculates the optimal ovulation date and timing of fertilization based on information such as the menstrual cycle and body temperature fluctuations.

[1732] The "means for eliminating the need for hospital visits for timing therapy" refers to a means that allows a user to perform timing therapy without visiting a medical institution.

[1733] "Methods for identifying genes that increase implantation rates in in vitro fertilization, intracytoplasmic sperm injection, etc." refers to the use of gene sequencing technology to identify specific genes that improve implantation rates.

[1734] "A means for predicting the date of ovulation from information such as menstrual cycle and body temperature fluctuations, and notifying the user of the optimal timing" is a means for analyzing the user's menstrual cycle and body temperature data, predicting the date of ovulation, and notifying the user of that information.

[1735] "Means for recording and analyzing a user's health data using a smartphone or smart glasses and notifying them in real time" refers to means for using a smartphone or smart glasses to collect and analyze a user's health data and notify the user of the results in real time.

[1736] A system for implementing the present invention is configured using hardware such as a server, a smartphone, smart glasses, etc. Specific embodiments are described below.

[1737] The server inputs data on men and women and provides a means to find the optimal timing therapy solution. This data includes information on age, health status, lifestyle habits, etc. The server analyzes this data and implements an algorithm that predicts the day of ovulation based on information such as menstrual cycle and temperature fluctuations. This algorithm is implemented using a programming language such as Python.

[1738] Users use their smartphones or smart glasses to record their health data, including the start date of their menstrual period and body temperature. This data is sent in real time to a server, which analyzes the data and calculates the optimal timing. The calculated results are then sent to the user via their smartphone or smart glasses.

[1739] As a specific example, consider a scenario in which a user uses a smartphone app to input the start date of their menstrual period, and the app predicts and notifies them of the next ovulation date. For example, if the user inputs the start dates of their menstrual period as September 1, 2023, September 29, 2023, and October 27, 2023, the app will predict the next ovulation date as November 10, 2023 and notify them.

[1740] Here's an example prompt that uses a generative AI model to analyze user data and calculate the optimal timing:

[1741] "When a user enters the first date of their period, the app will predict and notify them of their next ovulation date. For example, if a user enters the first date of their period on September 1, 2023, September 29, 2023, or October 27, 2023, the app will predict and notify them of their next ovulation date as November 10, 2023."

[1742] In this way, users can receive accurate predictions of their ovulation date and notifications of the optimal timing without having to visit a doctor.

[1743] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1744] Step 1:

[1745] The user uses a smartphone or smart glasses to input data such as the start date of their menstrual period and their body temperature. The input data is collected by the smartphone or smart glasses application. The input data includes the start date of their menstrual period (e.g., September 1, 2023, September 29, 2023, October 27, 2023) and body temperature data. This data serves as the basis for understanding the user's health condition.

[1746] Step 2:

[1747] The device (smartphone or smart glasses) sends the collected data to a server. The data includes the user's menstrual start date and body temperature. The server receives this data and stores it in a database. The data is sent in real time via the Internet.

[1748] Step 3:

[1749] The server analyzes the received data. Specifically, it calculates the average length of the menstrual cycle and predicts the next ovulation date. For example, if the menstrual start dates are September 1, 2023, September 29, 2023, and October 27, 2023, the server calculates the interval between these dates to determine the average cycle. It then subtracts 14 days from the average cycle and predicts the next ovulation date.

[1750] Step 4:

[1751] The server calculates the optimal timing based on the predicted ovulation date. The calculated timing is prepared as data to be notified to the user. For example, if the next ovulation date is predicted to be November 10, 2023, the server generates a message to notify the user of this date.

[1752] Step 5:

[1753] The server sends the generated notification message to the device. The notification message includes information about the predicted ovulation date and the optimal timing. The device displays the received notification message to the user. The user can receive the notification in real time via their smartphone or smart glasses.

[1754] Step 6:

[1755] The user can take appropriate action based on the information provided. For example, the information can be used to help manage the user's health, such as timing therapy to coincide with the predicted ovulation date. This makes it possible to receive accurate ovulation date predictions and notifications of the optimal timing without having to visit a doctor.

[1756] Example 3

[1757] Next, a third embodiment of the third embodiment will be described. 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."

[1758] Conventional infertility treatments include timing therapy, in vitro fertilization, and intracytoplasmic sperm injection (ICSI), but it has been difficult to find the optimal timing for these treatments and to identify genes that increase implantation rates. Furthermore, the need for outpatient visits places a significant burden on patients in terms of time and money. Furthermore, there has been a lack of optimal treatment proposals based on the results of genetic analysis, which often results in treatment effectiveness not being maximized.

[1759] 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.

[1760] In this invention, the server includes a means for a user to upload genetic information, a means for the server to receive and store the genetic information, a means for the server to analyze the genetic information, a means for the server to generate a prompt sentence based on the analysis results, a means for the server to send the prompt sentence to the generative AI model, a means for the generative AI model to generate a proposal, and a means for the server to provide the proposal to the user. This allows a user to upload genetic information from home, and the server analyzes the information and proposes the optimal treatment. This eliminates the need for hospital visits, reduces time and financial burdens, and maximizes the effectiveness of treatment.

[1761] "Gender data" refers to data about men and women, including information on age, health, lifestyle habits, etc.

[1762] "Timing therapy" is a treatment that involves having sexual intercourse at the optimal time to increase the chances of pregnancy.

[1763] "In vitro fertilization" is an infertility treatment in which eggs and sperm are fertilized outside the woman's body, and the fertilized eggs are then returned to the uterus.

[1764] "Intracytoplasmic sperm injection" is an infertility treatment method that uses a microscope to inject sperm directly into an egg to achieve fertilization.

[1765] "Implantation rate" refers to the success rate of a fertilized egg implanting in the uterine lining.

[1766] "Gene sequencing technology" is a technology for analyzing the base sequence of DNA.

[1767] The "means for users to upload genetic information" refers to an interface that allows users to provide their own genetic information to the system.

[1768] "Means for the server to receive and store genetic information" refers to the function by which the server receives genetic information provided by the user and safely stores it.

[1769] The "means by which the server analyzes genetic information" refers to a combination of software and hardware that allows the server to analyze the stored genetic information.

[1770] "Means for the server to generate a prompt sentence based on the analysis results" refers to the function by which the server creates a prompt sentence to be input into the generative AI model based on the analysis results of genetic information.

[1771] "Means for the server to send a prompt sentence to the generative AI model" refers to a communication means for the server to send the generated prompt sentence to the generative AI model.

[1772] "Means by which the generative AI model generates suggestions" refers to the function by which the generative AI model suggests the optimal treatment for the user based on a prompt sentence.

[1773] The "means by which the server provides suggestions to the user" is an interface by which the server notifies and provides suggestions from the generative AI model to the user.

[1774] This invention is a system that analyzes genetic information provided by a user, identifies genes that have the potential to increase implantation rates, and proposes optimal treatment methods. Specific embodiments of this system are described below.

[1775] First, users upload their genetic information through a dedicated web interface. This web interface is designed so that users can simply select a file and click the upload button. Once the user uploads their genetic information, the server receives the data and stores it in a secure database. When saving, a checksum is calculated to verify the integrity of the data, confirming that the data was saved correctly.

[1776] The server then uses gene sequencing software, such as Illumina's BaseSpace Sequence Hub, to analyze the stored genetic information. The server adds the analysis job to a queue and waits for the analysis to complete. Once the analysis is complete, the results are retrieved.

[1777] Based on the analysis results, the server generates a prompt. For example, if the analysis results indicate that a particular gene may increase the implantation rate, the server generates the following prompt:

[1778] "Based on the user's genetic information, we have determined that certain genes may increase implantation rates. Based on this information, we would like to suggest the most appropriate treatment for the user."

[1779] The server sends the generated prompt to a generative AI model (e.g., OpenAI's GPT-4). The transmission is done via an API. Based on the received prompt, the generative AI model generates a sentence proposing the optimal treatment for the user. For example, it might generate a suggestion such as, "According to the analysis results, a specific gene may increase the implantation rate. Therefore, we recommend that you consider treatment targeting this gene when undergoing in vitro fertilization or intracytoplasmic sperm injection."

[1780] Finally, the server provides the suggestions received from the generative AI model to the user, who can review the suggestions through a web interface. The server can also send email or push notifications to notify the user of the suggestions.

[1781] This system allows users to upload genetic information from home, and the server analyzes the information to suggest optimal treatments. This eliminates the need for hospital visits, reduces the time and financial burden, and maximizes the effectiveness of treatment. The flow of the identification process in Example 3 will be described with reference to Figure 15.

[1782] Step 1:

[1783] Users upload genetic information.

[1784] Users upload genetic information through a dedicated web interface. Specifically, users open a web browser, access the system's web interface, and log in by entering their username and password on the login screen. They then go to the genetic information upload page, click the "Select File" button, select a genetic information file from their local computer, and click the "Upload" button. The input is the genetic information file, and the output is the file being sent to the server.

[1785] Step 2:

[1786] The server receives and stores the genetic information.

[1787] The server receives the genetic information uploaded by the user. The received data is stored in a secure database. When saving, a checksum is calculated to verify the integrity of the data, and it is confirmed that the data was saved correctly. The input is the genetic information file sent by the user, and the output is the genetic information stored in the database.

[1788] Step 3:

[1789] The server analyzes the genetic information.

[1790] The server uses gene sequencing software to analyze the stored genetic information. Specifically, the server utilizes gene sequencing technologies such as Illumina's BaseSpace Sequence Hub. The server adds analysis jobs to a queue and waits until the analysis is complete. Once the analysis is complete, the server retrieves the results. The input is the genetic information stored in the database, and the output is the analysis results.

[1791] Step 4:

[1792] The server generates a prompt based on the analysis results.

[1793] The server generates a prompt sentence based on the analysis results. For example, if the analysis results indicate that a specific gene may increase the implantation rate, the server generates a prompt sentence such as, "Based on the user's genetic information, it has been determined that a specific gene may increase the implantation rate. Based on this information, please suggest the optimal treatment for the user." The input is the analysis results, and the output is the generated prompt sentence.

[1794] Step 5:

[1795] The server sends a prompt to the generative AI model.

[1796] The server sends the generated prompt sentence to the generative AI model. The transmission is done through an API. The server waits for a response from the generative AI model. The input is the generated prompt sentence, and the output is the transmission of the prompt sentence to the generative AI model.

[1797] Step 6:

[1798] A generative AI model generates suggestions.

[1799] Based on the received prompt, the generative AI model generates a sentence proposing the optimal treatment to the user. For example, it may generate a suggestion such as, "According to the analysis results, a specific gene may increase the implantation rate. Therefore, we recommend that you consider treatment targeting this gene when undergoing in vitro fertilization or intracytoplasmic sperm injection." The input is the prompt, and the output is the generated suggestion.

[1800] Step 7:

[1801] The server provides the suggestions to the user.

[1802] The server provides the suggestions received from the generative AI model to the user. The user can review the suggestions through a web interface. The server can also send email or push notifications to notify the user of the suggestions. The input is the generated suggestions, and the output is providing the suggestions to the user.

[1803] (Application example 3)

[1804] 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 headset type terminal 314 will be referred to as a "terminal."

[1805] In conventional IVF and ICSI, genetic testing was performed to increase implantation rates, but there was a lack of health risk assessment and preventative measures. Furthermore, there were also insufficient methods for finding optimal timing therapy or for eliminating the need for hospital visits. This made it difficult for users to comprehensively understand their own health status and take appropriate treatments and preventative measures.

[1806] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for inputting data on men and women and obtaining an optimal solution for timing therapy, means for eliminating the need for outpatient visits for timing therapy, means for identifying genes that increase the implantation rate in in vitro fertilization, intracytoplasmic sperm injection, etc., means for analyzing genetic information and assessing health risks, and means for generating a health risk report. This enables users to assess their health risks based on their own genetic information and take appropriate treatments or preventive measures.

[1807] "Gender data" refers to data about men and women, including information on age, health status, lifestyle habits, etc.

[1808] The "optimal solution for timing therapy" is a solution for administering treatment at the most effective timing based on data for men and women.

[1809] "Means for eliminating the need for hospital visits for timing therapy" refers to means for enabling a user to perform timing therapy at home without visiting a hospital.

[1810] "Genes that increase implantation rates" are genes that have the potential to improve the implantation rate of embryos in in vitro fertilization and intracytoplasmic sperm injection.

[1811] "Means for analyzing genetic information" refers to means for analyzing a DNA sample provided by a user and obtaining genetic data.

[1812] "Means for assessing health risks" refers to means for assessing the health risks of users based on the acquired genetic data.

[1813] The "means for generating a health risk report" refers to a means for generating a health risk report for a user based on the health risk assessment results.

[1814] The system for implementing this invention is composed of three main components: a server, a terminal, and a user. The role and specific processing of each component will be explained below.

[1815] server

[1816] The server plays a central role in analyzing genetic information, assessing health risks, and generating health risk reports. The following software is installed on the server:

[1817] Gene Sequencing Library (GeneSequencer): Analyzes DNA samples provided by users to obtain genetic data.

[1818] Health Risk Analysis Library (HealthRiskAnalyzer): Evaluates the user's health risk based on the acquired genetic data.

[1819] The server receives the user's DNA sample and analyzes the genetic information using a gene sequencing library. Next, it evaluates the health risk from the analyzed genetic information using a health risk analysis library. Finally, it generates a health risk report based on the evaluation results and provides it to the user.

[1820] Terminal

[1821] The terminal provides an interface for users to provide genetic information and receive health risk reports. The terminal includes the following features:

[1822] Uploading DNA samples: Users upload their own DNA samples to the server via their devices.

[1823] Display Health Risk Report: Displays the health risk report sent from the server to the user.

[1824] User

[1825] Users provide their DNA sample through the terminal and receive a health risk report. Users use the system by following the steps below:

[1826] 1. Use the terminal to upload your DNA sample to the server.

[1827] 2. The server analyzes the genetic information and assesses health risks.

[1828] 3. The health risk report generated by the server is received and displayed on the terminal.

[1829] Specific examples

[1830] For example, when a user uploads their DNA sample using a device, the server an...

Claims

1. A means for obtaining optimal timing therapy solutions by inputting data on men and women who wish to have children; A means for notifying at least one of the male and female of an optimal solution for timing therapy, thereby eliminating the need for hospital visits for timing therapy; A means for identifying a gene that increases the implantation rate of at least one of in vitro fertilization and intracytoplasmic sperm injection in the genetic information of at least one of the male and female; A means for generating a prompt sentence to be input to a generative AI model based on a discrimination result, inputting the generated prompt sentence to the generative AI model, and obtaining and providing an optimal treatment method from the generative AI model; A system including:

2. 2. The system of claim 1, further comprising means for analyzing the emotional state of at least one of the male and female using an emotional engine, and adjusting an optimal solution for timing therapy in response to the analysis of the emotional state.

3. The system according to claim 1, further comprising means for analyzing the emotional state of at least one of the male and female using an emotion engine, suggesting a visit to the hospital if the analyzed emotional state is good, and suggesting refraining from visiting the hospital if the user's emotional state is bad.

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