System

Abstract

To provide a system.SOLUTION: A system for providing information on infertility treatment, includes: means for referring to a database including at least one of past treatment results, statistical information, and information disclosure by research institutions; means for providing information on infertility treatment based on information obtained from the database; means for acquiring information from the database in response to an inquiry from a user; means for sending a prompt sentence to a generative AI model based on the acquired information; and means for returning a response from the generative AI model to the user.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] Infertility treatment faces the challenge of individual patients not being able to obtain sufficient information to select the treatment that is best suited to them. Specifically, in addition to the information provided by doctors, patients need more extensive and detailed information, such as past treatment results, statistical information, and information disclosure by research institutions. [Means for solving the problem]

[0005] The present invention provides a system that references a database containing past treatment results, statistical information, and information disclosure by research institutions, and provides information on infertility treatment based on this information. It also responds to inquiries from users based on information obtained from the database. Furthermore, by providing information on specific infertility treatment methods and responding to inquiries from users in a chat format, it provides information for individual patients to select the treatment that is best suited to them. [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 one arithmetic device or a combination of multiple arithmetic devices. Also, the processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit),

[0010] Examples include 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)).

[0011] 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.

[0012] 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.,

[0013] hard disk), or magnetic tape.

[0014] In the following embodiments, the coded communication I / F (Interface) refers to a communication processor and

[0015] The communication I / F is an interface that includes a network interface (NIC) and an antenna. The communication I / F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0016] 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."

[0017] [First embodiment]

[0018] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0019] 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.

[0020] 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).

[0021] 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.

[0022] 0 are connected to the bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0023] 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.

[0024] 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 includes an optical system including a lens, an aperture, and a shutter, and a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or CC

[0025] A compact digital camera equipped with an imaging element such as a D (Charge Coupled Device) image sensor.

[0026] It's a camera.

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

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

[0029] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0032] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0033] "Example 1"

[0034] One embodiment of the present invention is a system for providing infertility treatment information.

[0035] The system has a means to access a database that includes past treatment results, statistical information, and information disclosure by research institutions. Specifically, the database collects and organizes information on various infertility treatment methods, information on the treatment results and treatment process of patients who have received those treatment methods, and research results on infertility treatment that have been made public by various research institutions.

[0036] "Example 2"

[0037] The system has a means for responding to user inquiries based on information retrieved from the database. Specifically, when a user inquires about a specific infertility treatment method, the system retrieves information about the corresponding treatment method from the database and responds to the user based on that information. For example, when a user inquires, "What is the success rate of PGTA?", the system retrieves information about the success rate of PGTA from the database and provides it to the user.

[0038] "Example 3"

[0039] Furthermore, the system has a means for responding to inquiries from users in chat format. Specifically, when a user makes an inquiry in chat format, the system responds in real time. This response is generated based on information retrieved from the database. For example, if a user asks, "What are the advantages and disadvantages of two-stage transplantation?", the system retrieves information about the advantages and disadvantages of two-stage transplantation from the database and responds to the user based on that information.

[0040] The processing flow of each embodiment will be described below.

[0041] "Example 1"

[0042] Step 1: The system consults a database containing past treatment results, statistical information, and information disclosures from research institutions.

[0043] Step 2: Provide information about infertility treatment based on the information obtained from the database. "Example 2"

[0044] Step 1: Accept user inquiries.

[0045] Step 2: Retrieve information corresponding to the query from the database.

[0046] Step 3: Based on the obtained information, return a response to the user.

[0047] "Example 3"

[0048] Step 1: Accept chat-style inquiries from users.

[0049] Step 2: Retrieve information corresponding to the query from the database.

[0050] Step 3: Based on the information obtained, provide a real-time response to the user.

[0051] Example 1

[0052] 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."

[0053] Information on infertility treatment is diverse, making it difficult for patients and medical professionals to quickly and accurately obtain the information they need. Furthermore, there is a lack of systems for effectively utilizing past treatment results, statistical information, and information disclosure by research institutions. Furthermore, even if users enter specific search criteria, there are limited ways to provide relevant information. This makes it difficult for users to make decisions about the most appropriate treatment.

[0054] 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.

[0055] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, means for organizing collected data by category and storing it in the database, means for analyzing search conditions entered by the user and sending a request to the server, and means for displaying data returned from the server to the user. This allows users to quickly and accurately obtain the necessary infertility treatment information and make decisions to select the most appropriate treatment.

[0056] A "database" is a collection of information that can be stored and referenced, including past treatment results, statistical information, and information disclosure by research institutions.

[0057] A "means of reference" is a method or technique for searching information in a database and obtaining the required data.

[0058] "Means for providing" refers to the methods and techniques for displaying the acquired information to the user and providing it in a usable form.

[0059] "Means of responding" refers to the methods and techniques for returning answers to user inquiries based on information retrieved from a database.

[0060] "Organization means" refers to methods and techniques for classifying collected data into categories and storing them efficiently in a database.

[0061] "Means for parsing" refers to methods or techniques for understanding the search criteria entered by the user and generating an appropriate request based on that.

[0062] "Means for sending" refers to the method or technology for sending the parsed request to the server and obtaining the required information.

[0063] "Display means" refers to a method or technique for displaying data returned from the server in a format that is easy for the user to view.

[0064] This invention is a system for providing information on infertility treatment, and it operates in cooperation with three entities: a server, a terminal, and a user. The specific operation of each entity will be explained below.

[0065] Server Operation

[0066] The server plays a central role in collecting, organizing, and providing information about infertility treatment. The server uses the following hardware and software:

[0067] Hardware: powerful processor, sufficient memory, storage devices

[0068] Software: Database Management System (DBMS), API interface, data analysis tools

[0069] The server automatically collects information about infertility treatments from the internet and affiliated research institutions. For example, it accesses a research institution's database via API to obtain data on the "success rate of treatment method A" or the "side effects of treatment method B." The collected data is organized by category and stored in a database. The data is checked for duplication and inconsistencies, and cleansed as necessary.

[0070] In response to a user request, the server extracts the appropriate information from the database and returns it to the device, filtering the data based on the content of the request to provide the user with the most relevant information.

[0071] Device behavior

[0072] The terminal provides an interface for users to search and view infertility treatment information. The terminal uses the following hardware and software:

[0073] Hardware: Personal computers, smartphones, tablets

[0074] Software: web browser, mobile application

[0075] The device provides an interface that allows users to easily search for information. For example, it displays a web page or application with a search bar and filtering functions. When the user enters search criteria, the device sends the request to a server. When the request is sent, the device converts the user's input into an appropriate format that the server can understand.

[0076] The device receives the data returned from the server and displays it to the user, providing the displayed information in a visual format such as graphs and charts to help the user intuitively understand the information.

[0077] User Actions

[0078] Users use this system to obtain information about infertility treatment. Users operate the system in the following steps:

[0079] 1. Entering search criteria: The user enters search criteria through the device interface, such as "treatments suitable for women over 40" or "side effects of treatment method D." Filtering options are also available to refine the search criteria.

[0080] 2. Viewing information: The user views the information provided by the server on the device. For example, if the user searches for "success rate of treatment method E," the results are displayed on the device. The user can scroll through the displayed information to check the details, and can also save the information if necessary.

[0081] 3. Decision support: Users consider infertility treatment options based on the information provided. For example, a user might make a decision such as, "I see that treatment method F has a high success rate, so I decide to try that method." Tools are also available to compare multiple treatment methods and find the best option.

[0082] Examples and prompts

[0083] For example, if a user searches for "infertility treatments suitable for women over 35," the process will be as follows:

[0084] 1. A user types "fertility treatments suitable for women over 35" into the device's search bar.

[0085] 2. The device sends the request to the server.

[0086] 3. The server extracts the relevant information from the database and returns it to the terminal.

[0087] 4. The terminal displays the information to the user.

[0088] 5. The user considers the appropriate treatment based on the displayed information.

[0089] By inputting the following prompt sentence into the generative AI model, specific infertility treatment information can be obtained.

[0090] "Please tell me about infertility treatments suitable for women over 35. Please provide detailed information based on past treatment results, statistical information, and information disclosed by research institutions."

[0091] By inputting this prompt, the generative AI model provides relevant information to assist the user in making a decision.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] Data collection

[0095] The server automatically collects information about infertility treatments from the internet and affiliated research institutions. As input, it accesses the research institution's database via API to obtain data on the "success rate of treatment method A" and "side effects of treatment method B." As output, the collected data is saved in the server's temporary storage. Specifically, the server periodically schedules data collection and updates the database whenever new information is released.

[0096] Step 2:

[0097] Data organization

[0098] The server organizes the collected data by category and stores it in a database. As input, it receives the data stored in temporary storage. As output, the organized data is stored in the database. Specifically, the server checks for data duplication and inconsistency and cleanses the data as necessary. For example, if data for the same patient is collected multiple times, it will be merged into one.

[0099] Step 3:

[0100] Providing a user interface

[0101] The device provides an interface that allows users to easily search for information. As input, it receives the user's search criteria. As output, the search criteria are sent to the server. Specifically, the device displays a web page or application with a search bar and filtering function. It analyzes the search criteria entered by the user in real time and provides a suggestion function.

[0102] Step 4:

[0103] Submitting a Request

[0104] The terminal sends the search criteria entered by the user to the server. As input, it receives the user's search criteria. As output, it sends a request converted into an appropriate format to the server. In concrete terms, when sending a request, the terminal converts the user's input into an appropriate format so that the server can understand it.

[0105] Step 5:

[0106] Data provision

[0107] In response to a user request, the server extracts the appropriate information from the database and returns it to the terminal. As input, it receives the request sent from the terminal. As output, it returns the relevant information to the terminal. Specifically, the server filters data based on the content of the request and provides the user with the most relevant information.

[0108] Step 6:

[0109] Displaying the results

[0110] The terminal displays the data returned from the server to the user. As input, it receives the data returned from the server. As output, it displays the information in a format that is easy for the user to view. Specifically, the terminal provides the displayed information in a visual format such as graphs or charts, allowing the user to intuitively understand the information.

[0111] Step 7:

[0112] Viewing information

[0113] The user views the information displayed on the device. As input, the user receives the information displayed on the device. As output, the user checks the information and saves it if necessary. Specifically, the user scrolls through the displayed information to check the details, and uses the function to save the information if necessary.

[0114] Step 8:

[0115] Decision support

[0116] Based on the provided information, the user considers options for infertility treatment. As input, the user receives the information displayed on the device. As output, the user makes a decision to select the most appropriate treatment. Specifically, the user uses tools to compare multiple treatment methods and find the best option. For example, a comparison table is used to compare the success rate and side effects of each treatment method.

[0117] (Application example 1)

[0118] 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."

[0119] Couples and individuals considering infertility treatment need to collect and compare a lot of information to find the best treatment and medical institution. However, this information is scattered and difficult to collect efficiently. It is also difficult to obtain information based on the latest research results and treatment results, resulting in a lack of information to help users decide on the appropriate treatment and medical institution. For this reason, there is a need for a system that provides reliable information that is easily accessible to users.

[0120] 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.

[0121] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on information obtained from the database, means for proposing the most appropriate treatment based on the symptoms and desired treatment entered by the user, and means for searching for the most appropriate medical institution based on the user's location information and desired treatment. This enables users to efficiently obtain reliable infertility treatment information and select the most appropriate treatment and medical institution.

[0122] "Past treatment results" refers to data on the results and process of past infertility treatments.

[0123] "Statistical information" refers to information that is compiled and analyzed as numerical data on treatment results and treatment processes.

[0124] "Information disclosure by research institutions" refers to information such as research results and papers on infertility treatment that are made public by research institutions.

[0125] A "database" is a system that organizes and stores information including past treatment results, statistical information, and information disclosure by research institutions.

[0126] "Information on infertility treatment" refers to information such as infertility treatment methods, treatment results, treatment process, and the latest research results.

[0127] A "user inquiry" is a question or request for information made by a user to the system.

[0128] "Symptom" means any physical or medical condition related to infertility that you are experiencing.

[0129] "Desired treatment" refers to the type of infertility treatment the user wishes to undergo.

[0130] The "optimal treatment" is the infertility treatment method that best suits the user's symptoms and wishes.

[0131] "Location information" is data about a user's current location.

[0132] "Medical institutions" are facilities such as clinics and hospitals that provide infertility treatment.

[0133] "Chat format" refers to a format in which communication is conducted in real time using text messages.

[0134] A system for implementing this invention includes a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosure from research institutions, and provides information about infertility treatment to users. The user terminal is a device such as a smartphone or tablet, and provides an interface for users to access the system.

[0135] The server includes the following means:

[0136] 1. Database reference method: Refer to databases containing past treatment results, statistical information, and information disclosure by research institutions.

[0137] 2. Information provision method: Provide information about infertility treatment based on information obtained from the database.

[0138] 3. Inquiry response means: Responds to user inquiries based on information retrieved from the database.

[0139] 4. Treatment suggestion method: Suggests the most appropriate treatment based on the symptoms and desired treatment entered by the user.

[0140] 5. Medical institution search tool: Search for the most suitable medical institution based on the user's location and desired treatment.

[0141] Hardware and software used:

[0142] Hardware: Servers, smartphones, tablets

[0143] Software: Flask (a Python web framework), requests (a Python library for sending HTTP requests)

[0144] Data processing and calculation:

[0145] The server receives input data from the user (symptoms, desired treatment, location information) and searches the database for relevant information. The search results suggest the most suitable treatment and medical institution to the user. Specifically, the web application is built using Flask, and communication with the database is performed using the requests library.

[0146] Examples:

[0147] When a user opens the "Infertility Treatment Navigator" app and enters their symptoms (e.g., polycystic ovary syndrome) and desired treatment (e.g., in vitro fertilization), the server searches the database for the most appropriate treatment and medical institution and makes suggestions to the user.

[0148] Example prompt sentence:

[0149] A user has symptoms of Polycystic Ovarian Syndrome and wants to undergo IVF. Please suggest the best treatment and medical institution.

[0150] In this way, users can efficiently obtain reliable information on infertility treatments and select the most suitable treatment method and medical institution.

[0151] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0152] Step 1:

[0153] Users launch the application on their smartphone or tablet and enter their symptoms, desired treatment, and location information.

[0154] Input: Symptoms, desired treatment, location information

[0155] Output: User input data

[0156] Specific operation: The user enters symptoms (e.g., polycystic ovary syndrome), desired treatment (e.g., in vitro fertilization), and location information into the application's input form, and presses the submit button.

[0157] Step 2:

[0158] The user terminal sends the input data to the server.

[0159] Input: User-entered data

[0160] Output: Request to server

[0161] Specific operation: The user device sends input data to the server as an HTTP request.

[0162] Step 3:

[0163] The server analyzes the received user-entered data and searches the database for relevant treatment information.

[0164] Input: User-entered data

[0165] Output: Treatment information

[0166] What it does: The server uses Flask to parse the received data and uses the requests library to query the database and retrieve relevant treatment information.

[0167] Step 4:

[0168] Based on the treatment information obtained by the server, the system will suggest the most appropriate treatment for the user's symptoms and desired treatment.

[0169] Input: Treatment information, user-entered data

[0170] Output: Optimal treatment proposal

[0171] Specific operation: The server analyzes the treatment information it has acquired, and selects and proposes the treatment that best suits the user's symptoms and wishes.

[0172] Step 5:

[0173] The server searches for the most suitable medical institution based on the user's location and desired treatment.

[0174] Input: Location information, desired treatment

[0175] Output: Medical institution information

[0176] Specific operation: The server searches the database based on the location information and desired treatment, and obtains information on the most suitable medical institution.

[0177] Step 6:

[0178] The server sends optimal treatment suggestions and medical institution information to the user's terminal.

[0179] Input: Optimal treatment proposals, medical institution information

[0180] Output: Response to user terminal

[0181] Specific operation: The server sends the optimal treatment proposal and medical institution information to the user's terminal as an HTTP response.

[0182] Step 7:

[0183] The user device receives the response from the server and displays it to the user.

[0184] Input: Response from the server

[0185] Output: Information displayed to the user

[0186] Specific operation: The user device receives the response from the server and displays the optimal treatment suggestions and medical institution information on the application interface.

[0187] In this way, users can efficiently obtain reliable information on infertility treatments and select the most suitable treatment method and medical institution.

[0188] Example 2

[0189] 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."

[0190] Conventional infertility treatment information systems have made it difficult for users to quickly and accurately obtain the information they need. They also lacked the means to provide detailed information on specific infertility treatment methods, making it difficult to respond appropriately to user inquiries. Furthermore, the user interface was inadequate, and the environment for users to easily obtain information was not provided.

[0191] 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.

[0192] In this invention, the server includes a means for a user to input a query from a terminal, a means for the terminal to send the query to the server, a means for the server to analyze the query, a means for the server to access a database to acquire information, a means for the server to send the acquired information to the terminal, and a means for the terminal to display the information to the user, thereby enabling the user to quickly and accurately acquire the necessary infertility treatment information.

[0193] "User" means any person or entity that uses the System to obtain information.

[0194] A "terminal" is an electronic device used by a user to enter inquiries and receive information.

[0195] A "server" is a computer system that receives inquiries from users, analyzes them, accesses a database to obtain information, and sends it to a terminal.

[0196] An "inquiry" is a question or request that a user inputs to the system through a terminal.

[0197] A "database" is a data storage system for storing information including past treatment results, statistical information, and information disclosures at research institutions.

[0198] "Means for obtaining information" refers to the process by which the server accesses the database and searches for and obtains the required information.

[0199] The "means for displaying information" is a function that allows the terminal to display information received from the server in a format that is easy for the user to view.

[0200] "Specific infertility treatment method" refers to a specific infertility treatment method such as PGTA or two-stage transplantation.

[0201] "Chat format" is an interface format that allows a user to make inquiries and receive responses from the system in an interactive manner.

[0202] This invention is a system that allows users to quickly and accurately obtain information about infertility treatment using a terminal. The system operates by having the user input a query from the terminal and send the query to a server. The server analyzes the query, accesses a database to obtain the necessary information, and sends the obtained information to the terminal. The terminal then displays the received information to the user.

[0203] The system uses the following hardware and software:

[0204] Devices: Electronic devices such as smartphones, tablets, and computers

[0205] Server: a high-performance computer system

[0206] Database Management System (DBMS): MySQL (registered trademark)

[0207] Server software: Apache (registered trademark)

[0208] Programming language: Python

[0209] As a specific example of how it works, a user enters "What is the success rate of PGTA?" into the input field of the terminal. This query is sent from the terminal to the server, and the server accesses the MySQL database to obtain information about the success rate of PGTA. The server then sends the obtained information to the terminal, and the terminal displays to the user, "The success rate of PGTA is 60%."

[0210] Example prompt sentence:

[0211] User: What is the success rate of PGTA?

[0212] Server: Retrieving information from database...

[0213] Server: PGTA has a success rate of about 60%.

[0214] This system allows users to quickly and accurately obtain detailed information about specific infertility treatment methods. It also has a chat-style inquiry response function, allowing users to obtain information in an interactive format, greatly improving user convenience.

[0215] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0216] Step 1:

[0217] The user inputs a query from the terminal.

[0218] Specifically, the user opens a browser on their smartphone or computer and enters "What is the success rate of PGTA" in the search bar. The entered query is displayed in the input field on the device.

[0219] Input: A query entered by the user (e.g., "What is the success rate of PGTA?")

[0220] Output: The query displayed in the terminal input field

[0221] Step 2:

[0222] The terminal sends a query to the server.

[0223] The device sends the query entered by the user to the server as an HTTP request, in JSON format.

[0224] Input: A query entered by the user (e.g., "What is the success rate of PGTA?")

[0225] Output: HTTP request sent to the server (e.g., {"query": "What is the success rate of PGTA?"})

[0226] Step 3:

[0227] The server parses the query.

[0228] The server analyzes the received HTTP request and extracts the query. For example, it recognizes that the query is "What is the success rate of PGTA?"

[0229] Input: HTTP request sent from the device (e.g., {"query": "What is the success rate of PGTA?"})

[0230] Output: Parsed query (e.g. "What is the success rate of PGTA?")

[0231] Step 4:

[0232] The server accesses the database to retrieve the information.

[0233] The server sends a query to the database based on the request. For example, it executes the query "SELECT success_rate FROM treatments WHERE name='PGTA'". The database returns a success rate of "60%".

[0234] Input: Parsed query (e.g., "What is the success rate of PGTA?")

[0235] Output: Information retrieved from the database (e.g. "60%")

[0236] Step 5:

[0237] The server transmits the acquired information to the terminal.

[0238] The server converts the information retrieved from the database into JSON format and sends it to the terminal as an HTTP response. For example, it sends information such as "The success rate of PGTA is 60%."

[0239] Input: Information obtained from the database (e.g. "60%")

[0240] Output: HTTP response sent to the device (e.g., {"response": "PGTA success rate is 60%"})

[0241] Step 6:

[0242] The terminal displays the information to the user.

[0243] The terminal analyzes the information received from the server and displays it in a format that is easy for the user to read. For example, it displays on the screen, "The success rate of PGTA is 60%."

[0244] Input: HTTP response sent from the server (e.g., {"response": "PGTA success rate is 60%"})

[0245] Output: Information displayed to the user (e.g., "PGTA has a 60% success rate")

[0246] (Application example 2)

[0247] 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."

[0248] Conventional infertility treatment information systems are specialized in providing information about infertility treatment, so users have had the problem of not being able to obtain information about security. Also, a separate system is required to provide security information, which reduces convenience for users.

[0249] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information about infertility treatment based on information acquired from the database, a means for responding to inquiries from users based on the information acquired from the database, a means for referencing a database for providing security information, and a means for responding to users based on the security information acquired from the database. This makes it possible to provide both infertility treatment information and security information using a single system.

[0250] "Infertility treatment information" refers to any information related to infertility treatment, including past treatment results, statistical information, and information disclosed by research institutions.

[0251] A "database" is a collection of specific information that is systematically organized and stored so that it can be searched and retrieved as needed.

[0252] A "user inquiry" is a question or request made by a user of the system seeking specific information.

[0253] "Means for responding" refers to a method or device for providing appropriate information in response to a user's inquiry.

[0254] "Security information" refers to the latest security techniques and countermeasures for phishing scams, unauthorized access, etc.

[0255] "Chat format" is a method of exchanging information in a text-based interactive format in real time.

[0256] The system for implementing this invention consists of a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosure from research institutions, and provides appropriate information in response to user inquiries. It also references a database for providing security information, and responds to users with the latest security information.

[0257] The server uses the Flask framework to build a web application and manages information using an SQLite database. User devices, such as smartphones and PCs, access the server via the Internet. When a user requests specific information, the server retrieves the relevant information from the database and provides it to the user.

[0258] Specifically, when a user makes a query from their device such as "What are the latest phishing scams?", the server retrieves information about the latest phishing scams from the SQLite database and returns that information to the user, allowing the user to quickly obtain the information they need.

[0259] The hardware used is a computer as the server and a smartphone or PC as the user terminal. The software used is Flask (a Python framework) and SQLite (a database).

[0260] As a concrete example, if a user asks, "What are the latest phishing scams?" the server retrieves information about the latest phishing scams from the database and responds as follows:

[0261] "The latest phishing scam is using fake bank emails to steal personal information."

[0262] An example of a prompt for the generative AI model is as follows:

[0263] When a user asks, "What are the latest phishing scams?", retrieve information about the latest phishing scams from your database and respond with the following:

[0264] "The latest phishing scam is using fake bank emails to steal personal information."

[0265] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0266] Step 1:

[0267] The user inputs a query from a device. The user uses a smartphone or computer to input a question that requests specific information. For example, they might input, "What are the latest phishing scams?" The input data is a text query.

[0268] Step 2:

[0269] The terminal sends a query to the server. The query entered by the user is sent to the server via the Internet. The input data is the user's query text, and the output data is the query sent to the server.

[0270] Step 3:

[0271] The server receives the query and accesses the database. The server receives the query and accesses the SQLite database using the Flask framework. The input data is the user's query text and the output data is the result of executing the database query.

[0272] Step 4:

[0273] The server retrieves the relevant information from the database. The server executes an SQL query to retrieve information from the database that corresponds to the user's query. For example, information on "the latest phishing scam techniques." The input data is the SQL query, and the output data is the retrieved information.

[0274] Step 5:

[0275] The server generates a response based on the information it has obtained. The server generates a response to the user based on the information it has obtained. For example, it generates a response such as, "The latest phishing scam is to use fake bank emails to steal personal information." The input data is the obtained information, and the output data is the generated response text.

[0276] Step 6:

[0277] The server generates a response and sends it to the terminal. The server generates a response and sends it to the user's terminal. The input data is the generated response text, and the output data is the response sent to the terminal.

[0278] Step 7:

[0279] The device receives the response from the server and displays it to the user. The user's device displays the response received from the server. For example, it displays "The latest phishing scam uses fake bank emails to steal personal information." The input data is the response text from the server, and the output data is the response displayed to the user.

[0280] Example 3

[0281] 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."

[0282] Conventional infertility treatment information systems faced the challenge of being unable to respond quickly and appropriately to user inquiries. Furthermore, they lacked the means to provide detailed information on specific medical procedures, making it difficult for users to obtain the information they needed in a timely manner. Furthermore, the lack of a chat-style inquiry response function resulted in a poor user experience.

[0283] 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.

[0284] In this invention, the server includes means for receiving a user's inquiry, means for analyzing the inquiry, means for retrieving information from a database based on the analysis results, means for sending a prompt to the generative AI model based on the retrieved information, and means for returning a response from the generative AI model to the user. This enables prompt and appropriate responses to user inquiries. Furthermore, by including means for providing detailed information about specific medical procedures, the user can obtain the information they need in a timely manner. Furthermore, by including a chat-style inquiry response function, the user experience can be improved.

[0285] "Means for receiving inquiries from users" refers to the function that enables the server to receive inquiries sent by users via web browsers or mobile apps.

[0286] The "means for analyzing the inquiry" is a function for analyzing the content of the received inquiry and extracting keywords and intentions.

[0287] "Means for obtaining information from a database" refers to a function for obtaining necessary information from a database based on the analysis results.

[0288] "Means for sending prompt sentences to the generative AI model" is a function for sending prompt sentences generated based on the acquired information to the generative AI model.

[0289] "Means for returning a response from the generative AI model to the user" is a function for returning a response received from the generative AI model to the user.

[0290] The "means for providing information about a specific medical procedure" is a function for providing users with detailed information about a specific medical procedure.

[0291] "Means for responding to user inquiries in chat format" is a function for responding to user inquiries in chat format in real time.

[0292] MODE FOR CARRYING OUT THE INVENTION

[0293] The present invention is a system for quickly and appropriately responding to inquiries from users. A specific embodiment of this system will be described below.

[0294] 1. Generating the system program

[0295] The system program was developed using Python and runs as a web server using the Flask framework. It uses MySQL as the database and a common generative AI model (e.g., GPT-3 (registered trademark)).

[0296] 2. Program Processing

[0297] The server receives a query from the user and analyzes it. For analysis, it uses a text analysis library (e.g., NLTK or spaCy). Based on the analysis results, the server retrieves the necessary information from the database. Based on the retrieved information, the server sends a prompt to the generative AI model and returns the response from the generative AI model to the user.

[0298] 3. Specific Examples

[0299] Consider a case where a user uses a web browser or a mobile app to send a query such as "What are the advantages and disadvantages of two-stage porting?" In this case, the server processes the query as follows:

[0300] 1. A user sends a chat inquiry asking, "What are the advantages and disadvantages of two-stage transplant?"

[0301] 2. The server receives and analyzes this query.

[0302] 3. The server runs an SQL query to retrieve information about the "advantages and disadvantages of two-stage migration" from the MySQL database.

[0303] 4. Based on the information obtained, the following prompt sentence is input into the generative AI model:

[0304] Example prompt: "What are the advantages and disadvantages of a two-stage transplant? The advantages are ____ and the disadvantages are ____."

[0305] 5. A generative AI model (e.g., GPT-3) generates a response based on this prompt.

[0306] 6. The server generates a response and returns it to the user in real time.

[0307] In this way, the system can respond quickly and appropriately to inquiries from users. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0308] Step 1:

[0309] A user submits a query

[0310] A user sends a query in chat format using a web browser or a mobile app. For example, they type "What are the advantages and disadvantages of two-stage porting?" and click the send button. The input is the user's query, and the output is an HTTP request to the server.

[0311] Step 2:

[0312] The server receives the query

[0313] The server uses the Flask framework to receive an HTTP request from a user. The received request contains the user's query. The input is the HTTP request from the user, and the output is the text data of the query.

[0314] Step 3:

[0315] The server parses the query

[0316] The server analyzes the received query using a text analysis library (e.g., NLTK or spaCy). Through the analysis, keywords and intent are extracted. The input is the text data of the query, and the output is the analysis results (keywords and intent).

[0317] Step 4:

[0318] The server retrieves information from the database

[0319] The server retrieves the necessary information from the MySQL database based on the analysis results. For example, to retrieve information about "the advantages and disadvantages of two-stage migration," it executes an SQL query. The input is the analysis results, and the output is the information retrieved from the database.

[0320] Step 5:

[0321] The server sends a prompt to the generative AI model

[0322] The server generates a prompt based on the acquired information and sends it to the generative AI model. For example, it generates a prompt such as, "Please tell me about the advantages and disadvantages of two-stage transplantation. The advantages are XX and the disadvantages are △△." The input is information acquired from the database, and the output is a prompt sent to the generative AI model.

[0323] Step 6:

[0324] A generative AI model generates a response

[0325] The generative AI model generates a response based on the received prompt. For example, it might generate a response like, "The advantages of two-stage transplantation are a high success rate and a low burden on the patient. On the other hand, the disadvantages are that the surgery is complicated and time-consuming." The input is the prompt, and the output is the generated response.

[0326] Step 7:

[0327] The server returns the response to the user

[0328] The server returns the response received from the generative AI model to the user. Specifically, it returns the response as an HTTP response and displays it on the user's screen. The input is the generated response, and the output is the HTTP response to the user.

[0329] (Application example 3)

[0330] 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."

[0331] Conventional infertility treatment information systems have had the problem of being unable to respond quickly and accurately to user inquiries. Furthermore, there was a lack of detailed information on specific infertility treatment methods, which meant it took a long time for users to obtain the information they needed. Furthermore, there was a lack of technology to generate appropriate responses to user questions.

[0332] 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.

[0333] In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information on infertility treatment based on information obtained from the database, a means for responding to inquiries from users based on the information obtained from the database, and a means for generating appropriate responses to user questions using a generative AI model. This allows users to receive prompt and accurate responses, and detailed information on specific infertility treatment methods is also provided, enabling them to quickly obtain the information they need.

[0334] "Past treatment results" refers to data on the results and progress of previous infertility treatments.

[0335] "Statistical information" refers to data that shows the results of compiling and analyzing multiple treatment data.

[0336] "Information disclosure by research institutions" refers to research results and reports on infertility treatment that are made public by academic institutions and medical institutions.

[0337] A "database" is a system that systematically manages and stores data such as past treatment results, statistical information, and information disclosure by research institutions.

[0338] "Information on infertility treatment" refers to detailed data on infertility treatment methods, effects, side effects, success rates, etc.

[0339] "User inquiries" are actions by individuals using the system to ask questions or request information about infertility treatment.

[0340] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate appropriate responses to user questions.

[0341] An "appropriate response" is an answer that provides accurate and useful information to the user's question.

[0342] A system for carrying out this invention is configured as follows: The server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, and means for generating appropriate responses to user questions using a generative AI model.

[0343] Hardware and software used

[0344] Hardware: Servers, smartphones

[0345] Software: Python, SQLite, OpenAI (registered trademark) API

[0346] Data processing and calculation

[0347] The server first connects to a database using SQLite, which contains past treatment results, statistical information, and information disclosure by research institutions. When a user accesses the system using a smartphone and makes an inquiry via chat, the server analyzes the content of the inquiry.

[0348] Retrieving information from a database

[0349] If the user's query is about a specific fertility treatment method, the server retrieves the relevant information from the database and provides it to the user. For example, if the user asks, "What are the advantages and disadvantages of two-stage transplantation?", the server retrieves information about two-stage transplantation from the database and generates a response based on that information.

[0350] Using generative AI models

[0351] If the user's query is not directly related to the database, the server uses a generative AI model to generate an appropriate response. Specifically, it uses the OpenAI API to generate a prompt for the user's question, and the AI ​​generates a response based on that prompt.

[0352] Specific examples

[0353] For example, if a user asks, "What are the specs of the iPhone 13?", the server retrieves the specs of the iPhone 13 from the database and returns them to the user. Also, if a user asks, "What are the advantages and disadvantages of two-stage porting?", the server sends the following prompt to the generative AI model:

[0354] User Question: What are the advantages and disadvantages of a two-stage transplant?

[0355] answer:

[0356] Based on this prompt, the generative AI model generates an appropriate response and provides it to the user, allowing them to obtain information quickly and accurately.

[0357] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0358] Step 1:

[0359] Users access the system using their smartphones and make inquiries via chat.

[0360] Input: User question (e.g., "What are the advantages and disadvantages of two-stage transplantation?")

[0361] Output: The user's question is sent to the server.

[0362] Specific actions: A user opens a chat application on their smartphone, types a question, and presses the send button.

[0363] Step 2:

[0364] The server receives the user's question and analyzes its contents.

[0365] Input: User's question

[0366] Output: Analysis result of question (e.g. "Question about two-stage transplant")

[0367] Specific operation: The server analyzes the received text data using a natural language processing algorithm to identify the intent of the question.

[0368] Step 3:

[0369] The server connects to the database and looks up the appropriate information.

[0370] Input: Analysis result of question content

[0371] Output: Information retrieved from the database (e.g., "Information about the advantages and disadvantages of two-stage transplantation")

[0372] What happens next: The server queries the SQLite database and retrieves the relevant information.

[0373] Step 4:

[0374] The server generates a response based on information retrieved from the database.

[0375] Input: Information retrieved from the database

[0376] Output: Response to the user (e.g., "The advantages of two-stage porting are..., the disadvantages are...")

[0377] Specific behavior: The server formats the information it obtains into text and generates a response to send to the user.

[0378] Step 5:

[0379] The server uses the generative AI model to generate appropriate responses to the user's questions.

[0380] Input: User's question

[0381] Output: The response provided by the generative AI model (e.g., "The advantages and disadvantages of two-stage transplantation are...")

[0382] Specific behavior: The server sends a prompt to the OpenAI API and receives the generated response.

[0383] Step 6:

[0384] The server generates a response and sends it to the user.

[0385] Input: Generated response content

[0386] Output: A response message that will be displayed on the user's smartphone.

[0387] Specific behavior: The server sends the generated response to the user through the chat application, which displays it on the user's smartphone.

[0388] 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.

[0389] "Example 1"

[0390] One embodiment of the present invention is an infertility treatment information provision system that incorporates an emotion engine that recognizes the user's emotions. In this system, when a user makes an inquiry to the system, the emotion engine recognizes the user's emotions. The emotion engine analyzes emotions from the user's text input or voice input, and the results are used by the system. For example, if the user expresses anxiety, the system recognizes that emotion and provides information that will alleviate the anxiety.

[0391] "Example 2"

[0392] Another embodiment of the present invention is a system in which an emotion engine provides information about infertility treatment based on a user's emotions. In this system, the emotion engine analyzes the user's emotions and provides information appropriate to those emotions. For example, if the user expresses hopeful emotions, the system provides information about success stories and new treatments.

[0393] "Example 3"

[0394] In another embodiment of the present invention, an emotion engine responds to a user's inquiry based on the user's emotion. In this system, the emotion engine analyzes the user's emotion and generates a response appropriate to the emotion. For example, if the user is depressed, the system generates a response that includes words of encouragement.

[0395] The processing flow of each embodiment will be described below.

[0396] "Example 1"

[0397] Step 1: The user queries the system.

[0398] Step 2: The emotion engine analyzes emotions from the user's text or voice input.

[0399] Step 3: The system uses the analysis results of the emotion engine to provide information that matches the user's emotions.

[0400] "Example 2"

[0401] Step 1: The emotion engine analyzes the user's emotions.

[0402] Step 2: The system uses the analysis results of the emotion engine to provide information that matches the user's emotions.

[0403] "Example 3"

[0404] Step 1: The emotion engine analyzes the user's emotions.

[0405] Step 2: The system uses the analysis results of the emotion engine to generate a response that matches the user's emotions.

[0406] Example 1

[0407] 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."

[0408] Conventional infertility treatment information systems provide information without considering the user's feelings, and therefore are unable to fully alleviate the anxiety and stress felt by users. In addition, the lack of appropriate information based on the user's feelings has led to a problem of reduced user satisfaction.

[0409] 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.

[0410] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, emotion analysis means for recognizing the user's emotions, and means for providing appropriate information based on the emotions recognized by the emotion analysis means. This makes it possible to provide appropriate information according to the user's emotions, reducing user anxiety and stress and improving satisfaction.

[0411] "Past treatment results" refers to data on the results and process of past infertility treatments.

[0412] "Statistical information" is the result of compiling and analyzing data related to infertility treatment.

[0413] "Information disclosure by research institutions" refers to research results and reports on infertility treatment that are made public by research institutions.

[0414] A "database" is a system that organizes and stores information including past treatment results, statistical information, and information disclosure by research institutions.

[0415] "Emotion analysis means" is a technology that analyzes emotions from a user's text or voice input.

[0416] "Means for providing appropriate information" refers to technology that selects and provides information that is useful to the user based on the results of analyzing the user's emotions.

[0417] "Means for responding to inquiries" refers to technology that provides answers to questions and requests from users based on information retrieved from a database.

[0418] The present invention is a system for providing information on infertility treatment, which provides appropriate information to users by referencing a database containing past treatment results, statistical information, and information disclosure by research institutions. Furthermore, it has the function of recognizing the user's emotions and providing information based on those emotions.

[0419] Hardware and software used

[0420] 1. Server

[0421] The server uses a database management system (e.g., MySQL, PostgreSQL) to manage data including past treatment results, statistical information, and information disclosure at research institutions.

[0422] The server uses web server software (e.g., Apache, Nginx) to receive and process HTTP requests from users.

[0423] 2. Emotion analysis method

[0424] As a means of sentiment analysis, we use natural language processing APIs (e.g., Google® Cloud Natural Language API, IBM Watson® Tone Analyzer) to analyze emotions from the user's text and voice input.

[0425] 3. Terminal

[0426] Users access the system using a web browser (e.g., GOOGLE CHROME®, Safari) or a mobile app.

[0427] Data processing and calculation

[0428] 1. Database Reference

[0429] The server uses a database management system to access data including past treatment results, statistical information, and information disclosures from research institutions, thereby obtaining basic data to provide appropriate information in response to user inquiries.

[0430] 2. Emotion analysis

[0431] The server sends the user's text or voice input to the emotion analysis means, which analyzes the user's emotions. The analysis results are returned to the server in JSON format.

[0432] 3. Information provision

[0433] The server searches for appropriate information from the database based on the emotion analysis results and provides it to the user. For example, if the user expresses anxiety, it provides information on success stories and relaxation methods to alleviate anxiety.

[0434] Specific examples

[0435] For example, if a user enters the text "I'm worried because my recent treatment hasn't gone well," the process would proceed as follows:

[0436] 1. A user accesses the system using Google Chrome.

[0437] 2. The user types "I'm worried because my recent treatment wasn't going well" into the text box.

[0438] 3. The server receives this input through Apache.

[0439] 4. The server calls the Google Cloud Natural Language API and sends the input data.

[0440] 5. The emotion analysis means analyzes the emotion of "anxiety."

[0441] 6. The server receives the parsed results in JSON format.

[0442] 7. The server references the MySQL database.

[0443] 8. Search for information on success stories and relaxation techniques to help your server reduce anxiety.

[0444] 9. The server displays the search results on a web page and serves them to the user.

[0445] Prompt Sentence Examples

[0446] Examples of prompts to input to a generative AI model include:

[0447] If a user types, "I'm worried because my recent treatment didn't go well," explain how the emotion engine will respond and what information the server will provide.

[0448] Using this prompt, the generative AI model can recognize the user's emotions and understand the system's behavior to provide appropriate information.

[0449] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0450] Step 1:

[0451] A user accesses the system.

[0452] Users access the system using a web browser or a mobile app, for example, using a browser such as Google Chrome or Safari. The input is the user's access request, and the output is the system homepage being displayed.

[0453] Step 2:

[0454] The user enters a query.

[0455] The user enters a query using a text box or voice input function, for example, "I'm worried because my recent treatment didn't go well." The input is the user's text or voice data, and the output is that data being sent to the server.

[0456] Step 3:

[0457] The server receives the user's input.

[0458] The server receives an HTTP request and gets the user's input data. For example, we use a web server such as Apache or Nginx. The input is the user's query data, and the output is that data ready to be processed within the server.

[0459] Step 4:

[0460] The server invokes the emotion engine.

[0461] The server calls the emotion engine's API and sends the user's input data, for example, using Google Cloud Natural Language API or IBM Watson Tone Analyzer. The input is the user's text or voice data, and the output is the emotion analysis result.

[0462] Step 5:

[0463] The emotion engine analyzes the user's emotions.

[0464] The emotion engine analyzes the user's text or voice input to identify emotions, for example, recognizing the emotion "anxiety." The input is the user's text or voice data, and the output is the analyzed emotion data.

[0465] Step 6:

[0466] The server receives the analysis results.

[0467] The server receives the analysis results from the emotion engine and proceeds to the next step. For example, it receives the results in JSON format. The input is the emotion analysis results, and the output is that the results are available within the server.

[0468] Step 7:

[0469] The server references the database.

[0470] The server uses a database management system such as MySQL or PostgreSQL to access the database. The input is the sentiment analysis results and the user's query, and the output is the related information retrieved from the database.

[0471] Step 8:

[0472] The server looks up the appropriate information.

[0473] The server retrieves appropriate information from a database based on the user's emotions, for example, information about success stories and relaxation techniques to alleviate anxiety. The input is the information retrieved from the database, and the output is appropriate information to provide to the user.

[0474] Step 9:

[0475] The server provides the search results to the user.

[0476] The server returns the search results to the user, for example by displaying the results on a web page or by reading the results aloud. The input is the relevant information, and the output is the information presented to the user.

[0477] (Application example 1)

[0478] 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."

[0479] Infertility treatment information provision systems, the lack of information provided takes user emotions into consideration, which can cause users to feel anxious or stressed. Furthermore, conventional systems do not provide information in a virtual space, making it difficult for users to obtain information intuitively.

[0480] 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.

[0481] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information about infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, means for recognizing the user's emotions using an emotion engine that analyzes the user's emotions, means for providing appropriate information according to the recognized emotions, and means for providing an interface for viewing information in a virtual space. This makes it possible to provide appropriate information according to the user's emotions, allowing the user to intuitively obtain information in the virtual space.

[0482] A "database" is a system that organizes and stores information, including past treatment results, statistical information, and information disclosure by research institutions, and can be referenced as needed.

[0483] The "means for providing information about infertility treatment" is a function that provides users with information about infertility treatment based on information obtained from the database.

[0484] The "inquiry response means" is a function that responds to inquiries from users based on information obtained from the database.

[0485] An "emotion engine" is a technology that analyzes emotions from a user's text or voice input and makes the results available to the system.

[0486] The "emotion recognition means" is a function that recognizes the user's emotions using an emotion engine.

[0487] "Emotion response means" is a function that provides appropriate information according to the recognized emotion.

[0488] A "virtual interface" is an interface that allows a user to view information within a virtual space.

[0489] A system for implementing the present invention has the following configuration.

[0490] System configuration

[0491] 1. Database reference method

[0492] The server accesses a database containing past treatment results, statistical information, and information disclosures from research institutions. This database organizes and stores a variety of information related to infertility treatment, allowing for quick access when needed.

[0493] 2. Means of providing information on infertility treatment

[0494] The server uses the information retrieved from the database to provide users with information about fertility treatments, based on the specific treatments and statistical data they require.

[0495] 3. Inquiry response methods

[0496] The server responds to user inquiries based on information retrieved from the database, allowing users to obtain the information they need in real time.

[0497] 4. Emotion recognition means

[0498] The server recognizes the user's emotions using an emotion engine, which analyzes emotions from the user's text and voice input and makes the results available to the system.

[0499] 5. Emotional response measures

[0500] The server provides appropriate information depending on the emotion it recognizes. For example, if the user is feeling anxious, it provides reassuring information.

[0501] 6. Virtual Interfaces

[0502] The terminal provides an interface for users to view information in the virtual space, allowing users to obtain information intuitively.

[0503] Hardware and software used

[0504] Hardware: Smartphone, head-mounted display

[0505] Software: Python, emotion recognition library (EmotionEngine), database management system (TreatmentDatabase)

[0506] Data processing and calculation

[0507] 1. Getting User Input

[0508] The device receives text or voice input from the user.

[0509] 2. Emotion analysis

[0510] The server uses an emotion engine to analyze emotions from the user's input.

[0511] 3. Information provision

[0512] Based on the results of the sentiment analysis, the server retrieves appropriate information from the database and provides it to the user.

[0513] Specific examples

[0514] If a user types, "I'm worried about the results of my recent treatment," the emotion engine will interpret this as "anxiety," and the server will provide reassuring information.

[0515] If the user types, "I want to know about the latest treatments," the server provides general treatment information.

[0516] Prompt Sentence Examples

[0517] If a user types, "I'm worried about my recent treatment results," the emotion engine should interpret this as "anxiety" and provide reassuring information.

[0518] In this way, it becomes possible to provide appropriate information according to the user's emotions, realizing a system that allows users to obtain information intuitively within a virtual space.

[0519] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0520] Step 1:

[0521] The user inputs a query into the terminal by text or voice.

[0522] Input: User text or voice input (e.g., "I'm worried about my recent medical results")

[0523] Output: User input data

[0524] Specific operation: The device receives user input and stores it as text data or voice data.

[0525] Step 2:

[0526] The terminal transmits the user's input data to the server.

[0527] Input: User-entered data

[0528] Output: User input data sent to the server

[0529] Specific operation: The terminal transmits the user's input data to the server via the network.

[0530] Step 3:

[0531] The server uses an emotion engine to analyze emotions from the user's input data.

[0532] Input: User-entered data

[0533] Output: User's emotion data (e.g., "anxiety")

[0534] Specific operation: The server starts the emotion engine and analyzes the user's input data to identify emotions. The emotion engine performs text and voice analysis to classify the user's emotions into categories such as "anxiety," "relief," and "interest."

[0535] Step 4:

[0536] The server retrieves appropriate information from a database based on the emotion data.

[0537] Input: User emotion data (e.g., "anxiety")

[0538] Output: Appropriate information data (e.g., "reassuring information")

[0539] Specific operation: The server searches the database based on the emotion data and retrieves information corresponding to the user's emotion. For example, if the analysis finds that the emotion is "anxiety," it extracts information to reassure the user from the database.

[0540] Step 5:

[0541] The server transmits the acquired information to the terminal.

[0542] Input: Appropriate information data

[0543] Output: Information data sent to the terminal

[0544] Specific operation: The server transmits the acquired information data to the terminal via the network.

[0545] Step 6:

[0546] The terminal displays the received information to the user.

[0547] Input: Received information data

[0548] Output: Information displayed to the user

[0549] Specific operation: The terminal displays the received information data to the user. The display method can be a text display, a voice readout, or a display using a virtual interface.

[0550] In this way, it becomes possible to provide appropriate information according to the user's emotions, and a system is realized that allows users to obtain information intuitively within a virtual space.

[0551] Example 2

[0552] 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."

[0553] Conventional infertility treatment information systems can sometimes have difficulty responding appropriately and quickly to user inquiries. Furthermore, because information is not provided in response to users' emotions, user satisfaction may decline. Furthermore, because there is a lack of means to provide detailed information about specific infertility treatment methods, users may not be able to obtain the information they need.

[0554] 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.

[0555] In this invention, the server includes a means for receiving inquiries from users, a means for analyzing the received inquiries, a means for retrieving information from a database, a means for providing the retrieved information to the user, and a means for analyzing the user's emotions using an emotion engine and providing information appropriate to those emotions. This enables a quick and appropriate response to user inquiries, and provides information according to the user's emotions, thereby improving user satisfaction. In addition, detailed information on specific infertility treatment methods can be provided, allowing users to obtain sufficient information they need.

[0556] "Means for receiving inquiries from users" refers to an interface through which the system receives questions or requests entered by users.

[0557] "Means for analyzing the content of received inquiries" refers to a function for understanding received user inquiries using technologies such as natural language processing and extracting appropriate information.

[0558] The "means for acquiring information from a database" is a function for searching and acquiring related information from a database based on the analyzed query content.

[0559] The "means of providing the retrieved information to the user" is an interface that displays the information retrieved from the database in an easy-to-understand manner for the user.

[0560] "Means of analyzing a user's emotions using an emotion engine and providing information appropriate to those emotions" is a function that uses technology to analyze a user's emotions to select and provide information that corresponds to the user's emotional state.

[0561] "Means for providing information on specific infertility treatment methods" is a function for providing users with detailed information on specific infertility treatment methods, such as PGTA and two-stage transplantation.

[0562] The "means for responding to inquiries from users in a chat format" is an interface for conducting dialogue with users in a chat format and responding to inquiries in real time.

[0563] MODE FOR CARRYING OUT THE INVENTION

[0564] This invention is a system that responds to user inquiries based on information retrieved from a database. Specifically, when a user inquires about a specific infertility treatment method, the system retrieves information about the corresponding treatment method from the database and responds to the user based on that information. It also includes a function that uses an emotion engine to analyze the user's emotions and provide information appropriate to those emotions.

[0565] Hardware and software used

[0566] The server uses the following hardware and software:

[0567] Hardware: High-performance server machine (e.g., Intel Xeon processor, 32GB RAM, 1TB SSD)

[0568] software:

[0569] Database management system (e.g. MySQL)

[0570] Natural language processing engines (e.g., spaCy, NLTK)

[0571] Sentiment analysis engine (e.g. IBM Watson sentiment analysis API)

[0572] A terminal is a device through which a user inputs an inquiry and receives a response from the system. Terminals can be PCs, smartphones, tablets, etc.

[0573] Program processing explanation

[0574] When the server receives a user's inquiry, it analyzes the inquiry using a natural language processing engine. Based on the analysis results, the server retrieves the relevant information from the MySQL database. The retrieved information is sent to the terminal and displayed to the user.

[0575] Furthermore, the server analyzes the user's emotions using an emotion analysis engine. Based on the analysis results, it selects information appropriate to the user's emotions and sends it to the device. The device then displays the selected information to the user.

[0576] Specific examples

[0577] When a user asks "What is the success rate of PGTA?", the following steps are taken:

[0578] 1. The terminal receives a user query.

[0579] 2. The server uses a natural language processing engine to extract the keywords "PGTA" and "success rate."

[0580] 3. The server connects to the MySQL database and executes the following SQL query:

[0581] sql

[0582] SELECT success_rate FROM fertility_treatments WHERE treatment_name = 'PGTA';

[0583] 4. The server sends the acquired success rate data to the terminal.

[0584] 5. The terminal will display to the user, "PGTA success rate is 70%."

[0585] If the user expresses hopeful emotions, the server uses an emotion analysis engine to select information about success stories and new treatments based on the analysis results and sends it to the device, which then displays, "Recent research has led to the development of new treatments."

[0586] Prompt Sentence Examples

[0587] Below are some examples of prompts for generative AI models:

[0588] When a user asks, "What is the success rate of PGTA?", generate a program that retrieves information about the success rate of PGTA from the database and provides it to the user.

[0589]

[0590] If the user is expressing hopeful emotions, use the emotion engine to generate programs that provide information such as success stories and new treatments.

[0591] The above is an embodiment of the present invention.

[0592] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0593] Program processing flow

[0594] Step 1: Receive a user inquiry

[0595] The terminal receives a query from the user. For example, the user enters "What is the success rate of PGTA?" The entered query is sent from the terminal to the server.

[0596] Input: User query (e.g. "What is the success rate of PGTA?")

[0597] Output: Query data sent to the server

[0598] Step 2: Analyze the query

[0599] The server analyzes the received query using a natural language processing engine. Specifically, it uses NLP tools such as spaCy and NLTK to understand the intent of the query and extract important keywords.

[0600] Input: Inquiry data sent from the terminal

[0601] Data processing: Extract keywords using a natural language processing engine (e.g., "PGTA", "success rate")

[0602] Output: Extracted keywords

[0603] Step 3: Retrieving information from the database

[0604] The server retrieves the relevant information from the MySQL database based on the analysis results. For example, it executes an SQL query to retrieve information about the "PGTA success rate."

[0605] Input: Extracted keywords (e.g., "PGTA", "success rate")

[0606] Data Calculation: Execute an SQL query to retrieve information from the database (e.g. SELECT success_rate FROM fertility_treatments WHERE treatment_name = 'PGTA';)

[0607] Output: Retrieved information (e.g. "PGTA success rate is 70%")

[0608] Step 4: Provide information to users

[0609] The server sends the acquired information to the terminal, which then displays the received information to the user. For example, it displays "PGTA success rate is 70%."

[0610] Input: Retrieved information (e.g. "PGTA success rate is 70%")

[0611] Output: Information displayed to the user

[0612] Step 5: Provide information using the emotion engine (if necessary)

[0613] The server uses an emotion engine to analyze the user's emotions. For example, it uses IBM Watson's emotion analysis API. If the user expresses hopeful emotions, the server selects information about success stories and new treatments and sends it to the device. The device then displays the selected information to the user.

[0614] Input: User emotion data

[0615] Data processing: Analyze emotions using a sentiment analysis engine and select appropriate information

[0616] Output: Emotionally appropriate information displayed to the user (e.g., "Recent research has led to the development of a new treatment")

[0617] The above is the flow of processing of the program of this system.

[0618] (Application example 2)

[0619] 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."

[0620] There is a need for information provision systems related to infertility treatment to provide appropriate information according to the user's emotions. However, conventional systems provide uniform information without considering the user's emotions, making it difficult to reduce the psychological burden on the user.

[0621] 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.

[0622] In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information on infertility treatment based on information obtained from the database, a means for responding to inquiries from users based on the information obtained from the database, a means for analyzing the user's emotions, and a means for providing appropriate information based on the emotion analysis means. This makes it possible to provide appropriate information according to the user's emotions, thereby reducing the psychological burden on the user.

[0623] The "system for providing information on infertility treatment" refers to a database containing past treatment results, statistical information, and information disclosure by research institutions, and provides information on appropriate infertility treatment in response to user inquiries.

[0624] "Means for referencing databases" refers to means for obtaining necessary information from databases that include past treatment results, statistical information, and information disclosure by research institutions.

[0625] "Means for providing information on infertility treatment" refers to means for providing appropriate information on infertility treatment to users based on information obtained from the database.

[0626] "Means for responding to inquiries from users" refers to means for responding based on information obtained from the database when a user inquires about a specific infertility treatment method.

[0627] "Means for analyzing user emotions" refers to means for analyzing the user's facial expressions, tone of voice, etc. to determine the user's emotions.

[0628] The "means for providing appropriate information based on emotion analysis means" refers to a means for providing information on infertility treatment that is appropriate for the user's emotions based on the results of the user's emotion analysis.

[0629] This invention is a system for providing information on infertility treatment, which refers to a database containing past treatment results, statistical information, and information disclosure by research institutions, and provides appropriate information on infertility treatment in response to user inquiries. It also has the function of analyzing the user's emotions and providing appropriate information based on those emotions.

[0630] System configuration

[0631] The system consists of the following major components:

[0632] 1. Database reference method:

[0633] The server accesses a database containing a variety of information related to infertility treatment, including past treatment results, statistical information, and information disclosure by research institutions.

[0634] 2. Information provision method:

[0635] The server provides users with information about infertility treatments based on the information retrieved from the database, allowing users to learn about the latest treatments and success rates.

[0636] 3. Query response methods:

[0637] The server responds to user inquiries based on information retrieved from the database. For example, if a user asks, "What is the success rate of PGTA?", the server retrieves the relevant information from the database and provides it to the user.

[0638] 4. Emotion analysis means:

[0639] The server analyzes the user's facial expressions and tone of voice to determine their emotions, using the camera and microphone installed in the smart glasses.

[0640] 5. Emotion-based information delivery methods:

[0641] The server provides information appropriate to the user's emotions based on the emotion analysis method, for example, if the user expresses hopeful emotions, it provides information about success stories and new treatments.

[0642] Hardware and software used

[0643] Hardware:

[0644] Smart glasses (with camera and microphone)

[0645] server

[0646] software:

[0647] Python

[0648] OpenCV (image processing library)

[0649] EmotionRecognizer (emotion recognition library)

[0650] Database (database access library)

[0651] Processing flow

[0652] The server uses the smart glasses' camera and microphone to capture the user's facial expressions and voice. Using the captured data, the emotion recognition model analyzes the user's emotions. It then receives the user's inquiry and retrieves relevant information from the database. Based on the emotion analysis results, it provides additional information (such as success stories or new treatments). Finally, the captured information is displayed on the smart glasses.

[0653] Specific examples

[0654] For example, if a patient asks, "What is the success rate of PGTA?" and expresses hopeful sentiment, the smart glasses will display, "PGTA has a 70% success rate. We will also share recent success stories and new treatments."

[0655] Prompt Sentence Examples

[0656] Develop a smart glasses application that analyzes the patient's facial expressions and voice and provides information about fertility treatments based on their emotions. When a patient asks, "What is the success rate of PGTA?", retrieve information from a database and, if the patient expresses hopeful emotions, provide information about success stories and new treatments.

[0657] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0658] Step 1:

[0659] The server uses the camera and microphone of the smart glasses to capture the user's facial expressions and voice. The input is the user's facial image and voice data, and the output is these data. Specifically, the camera takes a picture of the user's face and the microphone records their voice.

[0660] Step 2:

[0661] The server runs an emotion recognition model using the acquired facial image and audio data. The input is the facial image and audio data acquired in step 1, and the output is the user's emotion (e.g., hopeful, pessimistic, etc.). Specifically, it uses the EmotionRecognizer library to analyze emotions from images and audio.

[0662] Step 3:

[0663] The server receives the user's query. The input is the user's voice or text, and the output is the query content. Specifically, it uses voice recognition technology to convert the user's voice into text and extracts the query content.

[0664] Step 4:

[0665] The server retrieves the relevant information from the database. The input is the inquiry retrieved in step 3, and the output is information about the relevant infertility treatment. Specifically, the server uses the Database library to search the database for information corresponding to the inquiry.

[0666] Step 5:

[0667] The server selects additional information based on the emotion analysis results. The input is the emotion data obtained in step 2 and the infertility treatment information obtained in step 4, and the output is additional information appropriate to the emotion. Specifically, if the emotion is hopeful, it selects information about success stories and new treatments.

[0668] Step 6:

[0669] The server displays the final information on the smart glasses. The input is the information selected in step 5, and the output is the information displayed on the smart glasses display. Specifically, the acquired information is displayed in text format on the smart glasses display.

[0670] Step 7:

[0671] The user reviews the information provided through the smart glasses. The input is the information displayed on the smart glasses' display, and the output is the user's understanding and next action. Specifically, the user reads the displayed information and considers the next treatment step or question.

[0672] Example 3

[0673] 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."

[0674] Conventional infertility treatment information systems struggled to provide prompt and appropriate responses to user inquiries. They also lacked the ability to generate responses based on the user's emotions, leaving the user experience open to improvement. Furthermore, they lacked detailed information about specific infertility treatment methods.

[0675] 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.

[0676] In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information on infertility treatment based on information obtained from the database, a means for responding to user inquiries based on the information obtained from the database, a means for generating responses in natural language using a generative AI model, and a means for analyzing user emotions and generating responses appropriate to those emotions. This makes it possible to provide quick and appropriate responses to user inquiries, and by generating responses appropriate to the user's emotions, the user experience can be improved.

[0677] A "database" is a collection of information including past treatment results, statistical information, and information disclosure by research institutions.

[0678] "Means for providing information about infertility treatment" is a function that provides information about infertility treatment to users based on information obtained from the database.

[0679] "Means for responding to inquiries from users" refers to a function that returns a response to an inquiry entered by a user based on information retrieved from a database.

[0680] A "generative AI model" is an artificial intelligence model used to generate natural language responses.

[0681] "Means for generating natural language responses" refers to the ability to use a generative AI model to generate appropriate responses in natural language to user inquiries.

[0682] The "means for analyzing emotions" is a function that analyzes the user's emotions and generates an appropriate response based on those emotions.

[0683] "Means of responding in chat format" refers to a function that provides a real-time response to user inquiries via a chat interface.

[0684] MODE FOR CARRYING OUT THE INVENTION

[0685] This invention is an infertility treatment information system that provides real-time responses to user inquiries. The system references a database containing past treatment results, statistical information, and information disclosures from research institutions, and generates responses based on the information obtained. It also uses a generative AI model to generate natural language responses and analyzes user sentiment to provide appropriate responses.

[0686] Hardware and software used

[0687] server

[0688] The server accesses the database and receives and processes user queries. The server uses the following software:

[0689] Database Management System (DBMS)

[0690] Generative AI models (e.g., GPT-4®)

[0691] Sentiment analysis software (e.g., IBM Watson's Tone Analyzer)

[0692] Terminal

[0693] A terminal is a device through which a user enters queries and receives and displays responses from the server. The terminal uses the following software:

[0694] Chat Interface

[0695] Data processing and calculation

[0696] Database Access

[0697] When the server receives a query from a user, it accesses the database to retrieve relevant information. For example, in response to the query "What are the advantages and disadvantages of two-stage transplantation?", the server searches the database for information about two-stage transplantation and retrieves the information about the advantages and disadvantages.

[0698] Response generation using generative AI models

[0699] The server generates a natural language response using a generative AI model (GPT-4) based on the acquired information. The generative AI model receives the query content and acquired information as input and generates an appropriate response.

[0700] Emotion analysis

[0701] The server uses emotion analysis software to analyze the user's emotions. For example, if the user is feeling down, the emotion analysis software will detect that emotion and generate a response that includes encouraging words.

[0702] Specific examples

[0703] Example 1: Inquiry about the advantages and disadvantages of two-stage transplantation

[0704] 1. A user types into the chat, "What are the advantages and disadvantages of a two-stage transplant?"

[0705] 2. The device sends this query to the server.

[0706] 3. The server receives the query and retrieves information about the two-stage migration from the database.

[0707] 4. Based on the information obtained by the server, a generative AI model (GPT-4) is used to generate a response.

[0708] 5. The server generates a response and sends it to the terminal.

[0709] 6. The device displays the following to the user: "The advantage of a two-stage transplant is that it has a higher success rate. The disadvantage is that the surgery is more complicated."

[0710] Example 2: Responding using the Emotion Engine

[0711] 1. A user types into the chat, "I've been feeling down lately because things haven't been going well at work."

[0712] 2. The device sends this query to the server.

[0713] 3. The server receives the query and the emotion analysis software analyzes the user's emotion.

[0714] 4. Sentiment analysis software detects the user's disappointment and generates a response with encouraging words.

[0715] 5. The server generates a response and sends it to the terminal.

[0716] 6. The device displays a message to the user saying, "That's tough, but you've overcome many challenges before. I'm sure you can get through this too."

[0717] In this way, the system can provide a quick and appropriate response to the user's inquiry and generate a response according to the user's emotion, thereby improving the user experience. The flow of the identification process in Example 3 will be described with reference to FIG.

[0718] Step 1:

[0719] The user inputs an inquiry in a chat format.

[0720] Specific operation: The user uses the chat interface on the device to input a question or inquiry, for example, "What are the advantages and disadvantages of two-stage transplantation?"

[0721] Input: User's inquiry

[0722] Output: Data sent to the terminal containing the query content

[0723] Step 2:

[0724] The terminal sends a query to the server.

[0725] Specific operation: The terminal sends the query entered by the user to the server in an appropriate format.

[0726] Input: User's inquiry

[0727] Output: The data sent to the server based on the query.

[0728] Step 3:

[0729] The server receives the query and accesses a database to retrieve the relevant information.

[0730] Specific operation: The server receives the query sent from the terminal and accesses the database to retrieve relevant information, such as the advantages and disadvantages of "two-stage transplantation."

[0731] Input: User's inquiry

[0732] Output: Relevant information retrieved from the database

[0733] Step 4:

[0734] The server generates a response using a generative AI model based on the information it obtains.

[0735] Specific operation: Based on the acquired information, the server generates a natural language response using a generative AI model (e.g., GPT-4). The generative AI model receives the query content and acquired information as input and generates an appropriate response.

[0736] Input: Related information obtained from the database, user inquiry

[0737] Output: The natural language response generated by the generative AI model

[0738] Step 5:

[0739] The server generates a response and sends it to the terminal.

[0740] Specific operation: The server sends the response generated by the generative AI model to the device in a format that is easy for the user to understand.

[0741] Input: A natural language response generated by a generative AI model

[0742] Output: Response data sent to the device

[0743] Step 6:

[0744] The terminal displays the response to the user.

[0745] Specific operation: The terminal displays the response received from the server to the user, and the user can check the response from the server through the chat interface.

[0746] Input: Response data sent from the server

[0747] Output: The response that is displayed to the user

[0748] Step 7:

[0749] The emotion engine analyzes the user's emotions and generates emotionally appropriate responses as needed.

[0750] What it does: The emotion engine analyzes the user's emotions. For example, if the user is feeling down, the emotion engine detects that emotion and generates a response that includes encouraging words. This emotion analysis is performed using emotion analysis software (e.g., IBM Watson's Tone Analyzer).

[0751] Input: User emotion data

[0752] Output: Emotionally appropriate response data

[0753] (Application example 3)

[0754] 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."

[0755] Conventional infertility treatment information systems can provide appropriate information in response to user inquiries, but they are unable to generate responses that reflect the user's emotions, which means they are unable to fully resolve the user's anxieties and doubts. Security services also need to provide appropriate support that reflects the user's emotions, but current systems have difficulty achieving this.

[0756] 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 referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information about infertility treatment based on information obtained from the database, means for responding to inquiries from users based on information obtained from the database, and means including an emotion engine for analyzing the user's emotions and generating a response appropriate to those emotions. This makes it possible to provide appropriate information and support according to the user's emotions.

[0757] A "database" is a system that stores information, including past treatment results, statistical information, and information disclosure by research institutions, and can be referenced as needed.

[0758] "Information provision means" is a function for providing users with information about infertility treatment based on information obtained from the database.

[0759] The "response means" is a function for generating a response to a user's inquiry based on information obtained from the database and sending it back to the user.

[0760] An "emotion engine" is a system that analyzes a user's emotions and generates a response appropriate to those emotions.

[0761] "Chat format" is a form of communication in which users make text-based inquiries and receive responses in real time.

[0762] The system for implementing this invention mainly consists of a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosures from research institutions, and provides appropriate information in response to user inquiries. It also uses an emotion engine to analyze the user's emotions and generate a response appropriate to those emotions.

[0763] Hardware and software used

[0764] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[0765] Software: Python, OpenAI API, EmotionRecognizer library, SecurityDatabase class

[0766] Data processing and calculation

[0767] When the server receives a user query, it first analyzes the query, retrieves relevant information from the database, and uses the EmotionRecognizer library to analyze the user's emotions. Based on the results of the emotion analysis, it generates an appropriate response using the OpenAI API.

[0768] Specific examples

[0769] For example, if a user enters "my home security camera isn't working," the server will process the following steps:

[0770] 1. Receive user input.

[0771] 2. Retrieve information about "Security Camera Troubleshooting" from the database.

[0772] 3. Analyze user emotions using the EmotionRecognizer library.

[0773] 4. If the user is concerned, use the OpenAI API to generate a response such as "Don't worry. Please check the power of your camera and restart it. If the problem persists, please contact support."

[0774] Prompt Sentence Examples

[0775] If the user types "My home security camera is not working," the following is an example of a prompt:

[0776] user_input = "My home security camera isn't working"

[0777] response = generate_response(user_input)

[0778] print(response)

[0779] In this way, it becomes possible to provide appropriate information and support according to the user's emotions.

[0780] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0781] Step 1:

[0782] A user uses a terminal to input a security query, for example, "The security camera in my house is not working." This input is sent to the server.

[0783] Input: User query (e.g., "My home security camera isn't working.")

[0784] Output: User query sent to server

[0785] Step 2:

[0786] The server analyzes the received user inquiry. Specifically, it performs text analysis of the inquiry content and extracts related keywords.

[0787] Input: User query

[0788] Output: Extracted keywords (e.g. "security camera", "not working")

[0789] Step 3:

[0790] The server searches the database based on the extracted keywords to obtain related information, for example, information on "security camera troubleshooting."

[0791] Input: Extracted keywords

[0792] Output: Relevant information retrieved from the database (e.g. "Please check the camera's power and restart it")

[0793] Step 4:

[0794] The server uses the EmotionRecognizer library to analyze emotions from the user's query, for example, to determine if the user is feeling anxious.

[0795] Input: User query

[0796] Output: Parsed user emotion (e.g. "anxiety")

[0797] Step 5:

[0798] The server uses the OpenAI API to generate an appropriate response based on the analyzed emotions, for example, if the user is feeling anxious, it generates a reassuring response.

[0799] Input: relevant information retrieved from the database, analyzed user sentiment

[0800] Output: The response generated (e.g. "Don't worry. Please check the power supply of your camera and reboot it. If the problem persists, please contact support.")

[0801] Step 6:

[0802] The server generates a response and sends it to the user's terminal, where the user can view the response.

[0803] Input: Generated response

[0804] Output: The response sent to the user's terminal

[0805] In this way, it becomes possible to provide appropriate information and support according to the user's emotions.

[0806] 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.

[0807] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[0808] An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[0809] , 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 the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0810] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0811] 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.

[0812] [Second embodiment]

[0813] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0814] 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.

[0815] 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).

[0816] 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.

[0817] 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.

[0818] 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).

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0825] "Example 1"

[0826] One embodiment of the present invention is a system for providing information on infertility treatment. This system has a means for referencing a database containing past treatment results, statistical information, and information disclosed by research institutions. Specifically, the database collects and organizes information on various infertility treatment methods, information on the treatment results and treatment process of patients who have received these treatment methods, and research results on infertility treatment published by various research institutions.

[0827] "Example 2"

[0828] The system has a means for responding to user inquiries based on information retrieved from the database. Specifically, when a user inquires about a specific infertility treatment method, the system retrieves information about the corresponding treatment method from the database and responds to the user based on that information. For example, when a user inquires, "What is the success rate of PGTA?", the system retrieves information about the success rate of PGTA from the database and provides it to the user.

[0829] "Example 3"

[0830] Furthermore, the system has a means for responding to inquiries from users in chat format. Specifically, when a user makes an inquiry in chat format, the system responds in real time. This response is generated based on information retrieved from the database. For example, if a user asks, "What are the advantages and disadvantages of two-stage transplantation?", the system retrieves information about the advantages and disadvantages of two-stage transplantation from the database and responds to the user based on that information.

[0831] The processing flow of each embodiment will be described below.

[0832] "Example 1"

[0833] Step 1: The system consults a database containing past treatment results, statistical information, and information disclosures from research institutions.

[0834] Step 2: Provide information about infertility treatment based on the information obtained from the database. "Example 2"

[0835] Step 1: Accept user inquiries.

[0836] Step 2: Retrieve information corresponding to the query from the database.

[0837] Step 3: Based on the obtained information, return a response to the user.

[0838] "Example 3"

[0839] Step 1: Accept chat-style inquiries from users.

[0840] Step 2: Retrieve information corresponding to the query from the database.

[0841] Step 3: Based on the information obtained, provide a real-time response to the user.

[0842] Example 1

[0843] 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."

[0844] Information on infertility treatment is diverse, making it difficult for patients and medical professionals to quickly and accurately obtain the information they need. Furthermore, there is a lack of systems for effectively utilizing past treatment results, statistical information, and information disclosure by research institutions. Furthermore, even if users enter specific search criteria, there are limited ways to provide relevant information. This makes it difficult for users to make decisions about the most appropriate treatment.

[0845] 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.

[0846] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, means for organizing collected data by category and storing it in the database, means for analyzing search conditions entered by the user and sending a request to the server, and means for displaying data returned from the server to the user. This allows users to quickly and accurately obtain the necessary infertility treatment information and make decisions to select the most appropriate treatment.

[0847] A "database" is a collection of information that can be stored and referenced, including past treatment results, statistical information, and information disclosure by research institutions.

[0848] A "means of reference" is a method or technique for searching information in a database and obtaining the required data.

[0849] "Means for providing" refers to the methods and techniques for displaying the acquired information to the user and providing it in a usable form.

[0850] "Means of responding" refers to the methods and techniques for returning answers to user inquiries based on information retrieved from a database.

[0851] "Organization means" refers to methods and techniques for classifying collected data into categories and storing them efficiently in a database.

[0852] "Means for parsing" refers to methods or techniques for understanding the search criteria entered by the user and generating an appropriate request based on that.

[0853] "Means for sending" refers to the method or technology for sending the parsed request to the server and obtaining the required information.

[0854] "Display means" refers to a method or technique for displaying data returned from the server in a format that is easy for the user to view.

[0855] This invention is a system for providing information on infertility treatment, and it operates in cooperation with three entities: a server, a terminal, and a user. The specific operation of each entity will be explained below.

[0856] Server Operation

[0857] The server plays a central role in collecting, organizing, and providing information about infertility treatment. The server uses the following hardware and software:

[0858] Hardware: powerful processor, sufficient memory, storage devices

[0859] Software: Database Management System (DBMS), API interface, data analysis tools

[0860] The server automatically collects information about infertility treatments from the internet and affiliated research institutions. For example, it accesses a research institution's database via API to obtain data on the "success rate of treatment method A" or the "side effects of treatment method B." The collected data is organized by category and stored in a database. The data is checked for duplication and inconsistencies, and cleansed as necessary.

[0861] In response to a user request, the server extracts the appropriate information from the database and returns it to the device, filtering the data based on the content of the request to provide the user with the most relevant information.

[0862] Device behavior

[0863] The terminal provides an interface for users to search and view infertility treatment information. The terminal uses the following hardware and software:

[0864] Hardware: Personal computers, smartphones, tablets

[0865] Software: web browser, mobile application

[0866] The device provides an interface that allows users to easily search for information. For example, it displays a web page or application with a search bar and filtering functions. When the user enters search criteria, the device sends the request to a server. When the request is sent, the device converts the user's input into an appropriate format that the server can understand.

[0867] The device receives the data returned from the server and displays it to the user, providing the displayed information in a visual format such as graphs and charts to help the user intuitively understand the information.

[0868] User Actions

[0869] Users use this system to obtain information about infertility treatment. Users operate the system in the following steps:

[0870] 1. Entering search criteria: The user enters search criteria through the device interface, such as "treatments suitable for women over 40" or "side effects of treatment method D." Filtering options are also available to refine the search criteria.

[0871] 2. Viewing information: The user views the information provided by the server on the device. For example, if the user searches for "success rate of treatment method E," the results are displayed on the device. The user can scroll through the displayed information to check the details, and can also save the information if necessary.

[0872] 3. Decision support: Users consider infertility treatment options based on the information provided. For example, a user might make a decision such as, "I see that treatment method F has a high success rate, so I decide to try that method." Tools are also available to compare multiple treatment methods and find the best option.

[0873] Examples and prompts

[0874] For example, if a user searches for "infertility treatments suitable for women over 35," the process will be as follows:

[0875] 1. A user types "fertility treatments suitable for women over 35" into the device's search bar.

[0876] 2. The device sends the request to the server.

[0877] 3. The server extracts the relevant information from the database and returns it to the terminal.

[0878] 4. The terminal displays the information to the user.

[0879] 5. The user considers the appropriate treatment based on the displayed information.

[0880] By inputting the following prompt sentence into the generative AI model, specific infertility treatment information can be obtained.

[0881] "Please tell me about infertility treatments suitable for women over 35. Please provide detailed information based on past treatment results, statistical information, and information disclosed by research institutions."

[0882] By inputting this prompt, the generative AI model provides relevant information to assist the user in making decisions.

[0883] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0884] Step 1:

[0885] Data collection

[0886] The server automatically collects information about infertility treatments from the internet and affiliated research institutions. As input, it accesses the research institution's database via API to obtain data on the "success rate of treatment method A" and "side effects of treatment method B." As output, the collected data is saved in the server's temporary storage. Specifically, the server periodically schedules data collection and updates the database whenever new information is released.

[0887] Step 2:

[0888] Data organization

[0889] The server organizes the collected data by category and stores it in a database. As input, it receives the data stored in temporary storage. As output, the organized data is stored in the database. Specifically, the server checks for data duplication and inconsistency and cleanses the data as necessary. For example, if data for the same patient is collected multiple times, it will be merged into one.

[0890] Step 3:

[0891] Providing a user interface

[0892] The device provides an interface that allows users to easily search for information. As input, it receives the user's search criteria. As output, the search criteria are sent to the server. Specifically, the device displays a web page or application with a search bar and filtering function. It analyzes the search criteria entered by the user in real time and provides a suggestion function.

[0893] Step 4:

[0894] Submitting a Request

[0895] The terminal sends the search criteria entered by the user to the server. As input, it receives the user's search criteria. As output, it sends a request converted into an appropriate format to the server. In concrete terms, when sending a request, the terminal converts the user's input into an appropriate format so that the server can understand it.

[0896] Step 5:

[0897] Data provision

[0898] In response to a user request, the server extracts the appropriate information from the database and returns it to the terminal. As input, it receives the request sent from the terminal. As output, it returns the relevant information to the terminal. Specifically, the server filters data based on the content of the request and provides the user with the most relevant information.

[0899] Step 6:

[0900] Displaying the results

[0901] The terminal displays the data returned from the server to the user. As input, it receives the data returned from the server. As output, it displays the information in a format that is easy for the user to view. Specifically, the terminal provides the displayed information in a visual format such as graphs or charts, allowing the user to intuitively understand the information.

[0902] Step 7:

[0903] Viewing information

[0904] The user views the information displayed on the device. As input, the user receives the information displayed on the device. As output, the user checks the information and saves it if necessary. Specifically, the user scrolls through the displayed information to check the details, and uses the function to save the information if necessary.

[0905] Step 8:

[0906] Decision support

[0907] Based on the provided information, the user considers options for infertility treatment. As input, the user receives the information displayed on the device. As output, the user makes a decision to select the most appropriate treatment. Specifically, the user uses tools to compare multiple treatment methods and find the best option. For example, a comparison table is used to compare the success rate and side effects of each treatment method.

[0908] (Application example 1)

[0909] 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."

[0910] Couples and individuals considering infertility treatment need to collect and compare a lot of information to find the best treatment and medical institution. However, this information is scattered and difficult to collect efficiently. It is also difficult to obtain information based on the latest research results and treatment results, resulting in a lack of information to help users decide on the appropriate treatment and medical institution. For this reason, there is a need for a system that provides reliable information that is easily accessible to users.

[0911] 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.

[0912] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on information obtained from the database, means for proposing the most appropriate treatment based on the symptoms and desired treatment entered by the user, and means for searching for the most appropriate medical institution based on the user's location information and desired treatment. This enables users to efficiently obtain reliable infertility treatment information and select the most appropriate treatment and medical institution.

[0913] "Past treatment results" refers to data on the results and process of past infertility treatments.

[0914] "Statistical information" refers to information that is compiled and analyzed as numerical data on treatment results and treatment processes.

[0915] "Information disclosure by research institutions" refers to information such as research results and papers on infertility treatment that are made public by research institutions.

[0916] A "database" is a system that organizes and stores information including past treatment results, statistical information, and information disclosure by research institutions.

[0917] "Information on infertility treatment" refers to information such as infertility treatment methods, treatment results, treatment process, and the latest research results.

[0918] A "user inquiry" is a question or request for information made by a user to the system.

[0919] "Symptom" means any physical or medical condition related to infertility that you are experiencing.

[0920] "Desired treatment" refers to the type of infertility treatment the user wishes to undergo.

[0921] The "optimal treatment" is the infertility treatment method that best suits the user's symptoms and wishes.

[0922] "Location information" is data about a user's current location.

[0923] "Medical institutions" are facilities such as clinics and hospitals that provide infertility treatment.

[0924] "Chat format" refers to a format in which communication is conducted in real time using text messages.

[0925] A system for implementing this invention includes a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosure from research institutions, and provides information about infertility treatment to users. The user terminal is a device such as a smartphone or tablet, and provides an interface for users to access the system.

[0926] The server includes the following means:

[0927] 1. Database reference method: Refer to databases containing past treatment results, statistical information, and information disclosure by research institutions.

[0928] 2. Information provision method: Provide information about infertility treatment based on information obtained from the database.

[0929] 3. Inquiry response means: Responds to user inquiries based on information retrieved from the database.

[0930] 4. Treatment suggestion method: Suggests the most appropriate treatment based on the symptoms and desired treatment entered by the user.

[0931] 5. Medical institution search tool: Search for the most suitable medical institution based on the user's location and desired treatment.

[0932] Hardware and software used:

[0933] Hardware: Servers, smartphones, tablets

[0934] Software: Flask (a Python web framework), requests (a Python library for sending HTTP requests)

[0935] Data processing and calculation:

[0936] The server receives input data from the user (symptoms, desired treatment, location information) and searches the database for relevant information. The search results suggest the most suitable treatment and medical institution to the user. Specifically, the web application is built using Flask, and communication with the database is performed using the requests library.

[0937] Examples:

[0938] When a user opens the "Infertility Treatment Navigator" app and enters their symptoms (e.g., polycystic ovary syndrome) and desired treatment (e.g., in vitro fertilization), the server searches the database for the most appropriate treatment and medical institution and makes suggestions to the user.

[0939] Example prompt sentence:

[0940] A user has symptoms of Polycystic Ovarian Syndrome and wants to undergo IVF. Please suggest the best treatment and medical institution.

[0941] In this way, users can efficiently obtain reliable information on infertility treatments and select the most suitable treatment method and medical institution.

[0942] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0943] Step 1:

[0944] Users launch the application on their smartphone or tablet and enter their symptoms, desired treatment, and location information.

[0945] Input: Symptoms, desired treatment, location information

[0946] Output: User input data

[0947] Specific operation: The user enters symptoms (e.g., polycystic ovary syndrome), desired treatment (e.g., in vitro fertilization), and location information into the application's input form, and presses the submit button.

[0948] Step 2:

[0949] The user terminal sends the input data to the server.

[0950] Input: User-entered data

[0951] Output: Request to server

[0952] Specific operation: The user device sends input data to the server as an HTTP request.

[0953] Step 3:

[0954] The server analyzes the received user-entered data and searches the database for relevant treatment information.

[0955] Input: User-entered data

[0956] Output: Treatment information

[0957] What it does: The server uses Flask to parse the received data and uses the requests library to query the database and retrieve relevant treatment information.

[0958] Step 4:

[0959] Based on the treatment information obtained by the server, the system will suggest the most appropriate treatment for the user's symptoms and desired treatment.

[0960] Input: Treatment information, user-entered data

[0961] Output: Optimal treatment proposal

[0962] Specific operation: The server analyzes the treatment information it has acquired, and selects and proposes the treatment that best suits the user's symptoms and wishes.

[0963] Step 5:

[0964] The server searches for the most suitable medical institution based on the user's location and desired treatment.

[0965] Input: Location information, desired treatment

[0966] Output: Medical institution information

[0967] Specific operation: The server searches the database based on the location information and desired treatment, and obtains information on the most suitable medical institution.

[0968] Step 6:

[0969] The server sends optimal treatment suggestions and medical institution information to the user's terminal.

[0970] Input: Optimal treatment proposals, medical institution information

[0971] Output: Response to user terminal

[0972] Specific operation: The server sends the optimal treatment proposal and medical institution information to the user's terminal as an HTTP response.

[0973] Step 7:

[0974] The user device receives the response from the server and displays it to the user.

[0975] Input: Response from the server

[0976] Output: Information displayed to the user

[0977] Specific operation: The user device receives the response from the server and displays the optimal treatment suggestions and medical institution information on the application interface.

[0978] In this way, users can efficiently obtain reliable information on infertility treatments and select the most suitable treatment method and medical institution.

[0979] Example 2

[0980] 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."

[0981] Conventional infertility treatment information systems have made it difficult for users to quickly and accurately obtain the information they need. They also lacked the means to provide detailed information on specific infertility treatment methods, making it difficult to respond appropriately to user inquiries. Furthermore, the user interface was inadequate, and the environment for users to easily obtain information was not provided.

[0982] 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.

[0983] In this invention, the server includes a means for a user to input a query from a terminal, a means for the terminal to send the query to the server, a means for the server to analyze the query, a means for the server to access a database to acquire information, a means for the server to send the acquired information to the terminal, and a means for the terminal to display the information to the user, thereby enabling the user to quickly and accurately acquire the necessary infertility treatment information.

[0984] "User" means any person or entity that uses the System to obtain information.

[0985] A "terminal" is an electronic device used by a user to enter inquiries and receive information.

[0986] A "server" is a computer system that receives inquiries from users, analyzes them, accesses a database to obtain information, and sends it to a terminal.

[0987] An "inquiry" is a question or request that a user inputs to the system through a terminal.

[0988] A "database" is a data storage system for storing information including past treatment results, statistical information, and information disclosures at research institutions.

[0989] "Means for obtaining information" refers to the process by which the server accesses the database and searches for and obtains the required information.

[0990] The "means for displaying information" is a function that allows the terminal to display information received from the server in a format that is easy for the user to view.

[0991] "Specific infertility treatment method" refers to a specific infertility treatment method such as PGTA or two-stage transplantation.

[0992] "Chat format" is an interface format that allows a user to make inquiries and receive responses from the system in an interactive manner.

[0993] This invention is a system that allows users to quickly and accurately obtain information about infertility treatment using a terminal. The system operates by having the user input a query from the terminal and send the query to a server. The server analyzes the query, accesses a database to obtain the necessary information, and sends the obtained information to the terminal. The terminal then displays the received information to the user.

[0994] The system uses the following hardware and software:

[0995] Devices: Electronic devices such as smartphones, tablets, and computers

[0996] Server: a high-performance computer system

[0997] Database Management System (DBMS): MySQL

[0998] Server software:Apache

[0999] Programming language: Python

[1000] As a specific example of how it works, a user enters "What is the success rate of PGTA?" into the input field of the terminal. This query is sent from the terminal to the server, and the server accesses the MySQL database to obtain information about the success rate of PGTA. The server then sends the obtained information to the terminal, and the terminal displays to the user, "The success rate of PGTA is 60%."

[1001] Example prompt sentence:

[1002] User: What is the success rate of PGTA?

[1003] Server: Retrieving information from database...

[1004] Server: PGTA has a success rate of about 60%.

[1005] This system allows users to quickly and accurately obtain detailed information about specific infertility treatment methods. It also has a chat-style inquiry response function, allowing users to obtain information in an interactive format, greatly improving user convenience.

[1006] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1007] Step 1:

[1008] The user inputs a query from the terminal.

[1009] Specifically, the user opens a browser on their smartphone or computer and enters "What is the success rate of PGTA" in the search bar. The entered query is displayed in the input field on the device.

[1010] Input: A query entered by the user (e.g., "What is the success rate of PGTA?")

[1011] Output: The query displayed in the terminal input field

[1012] Step 2:

[1013] The terminal sends a query to the server.

[1014] The device sends the query entered by the user to the server as an HTTP request, in JSON format.

[1015] Input: A query entered by the user (e.g., "What is the success rate of PGTA?")

[1016] Output: HTTP request sent to the server (e.g., {"query": "What is the success rate of PGTA?"})

[1017] Step 3:

[1018] The server parses the query.

[1019] The server analyzes the received HTTP request and extracts the query. For example, it recognizes that the query is "What is the success rate of PGTA?"

[1020] Input: HTTP request sent from the device (e.g., {"query": "What is the success rate of PGTA?"})

[1021] Output: Parsed query (e.g. "What is the success rate of PGTA?")

[1022] Step 4:

[1023] The server accesses the database to retrieve the information.

[1024] The server sends a query to the database based on the request. For example, it executes the query "SELECT success_rate FROM treatments WHERE name='PGTA'". The database returns a success rate of "60%".

[1025] Input: Parsed query (e.g., "What is the success rate of PGTA?")

[1026] Output: Information retrieved from the database (e.g. "60%")

[1027] Step 5:

[1028] The server transmits the acquired information to the terminal.

[1029] The server converts the information retrieved from the database into JSON format and sends it to the terminal as an HTTP response. For example, it sends information such as "The success rate of PGTA is 60%."

[1030] Input: Information obtained from the database (e.g. "60%")

[1031] Output: HTTP response sent to the device (e.g., {"response": "PGTA success rate is 60%"})

[1032] Step 6:

[1033] The terminal displays the information to the user.

[1034] The terminal analyzes the information received from the server and displays it in a format that is easy for the user to read. For example, it displays on the screen, "The success rate of PGTA is 60%."

[1035] Input: HTTP response sent from the server (e.g., {"response": "PGTA success rate is 60%"})

[1036] Output: Information displayed to the user (e.g., "PGTA has a 60% success rate")

[1037] (Application example 2)

[1038] 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."

[1039] Conventional infertility treatment information systems are specialized in providing information about infertility treatment, so users have had the problem of not being able to obtain information about security. Also, a separate system is required to provide security information, which reduces convenience for users.

[1040] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information about infertility treatment based on information acquired from the database, a means for responding to inquiries from users based on the information acquired from the database, a means for referencing a database for providing security information, and a means for responding to users based on the security information acquired from the database. This makes it possible to provide both infertility treatment information and security information using a single system.

[1041] "Infertility treatment information" refers to any information related to infertility treatment, including past treatment results, statistical information, and information disclosed by research institutions.

[1042] A "database" is a collection of specific information that is systematically organized and stored so that it can be searched and retrieved as needed.

[1043] A "user inquiry" is a question or request made by a user of the system seeking specific information.

[1044] "Means for responding" refers to a method or device for providing appropriate information in response to a user's inquiry.

[1045] "Security information" refers to the latest security techniques and countermeasures for phishing scams, unauthorized access, etc.

[1046] "Chat format" is a method of exchanging information in a text-based interactive format in real time.

[1047] The system for implementing this invention consists of a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosure from research institutions, and provides appropriate information in response to user inquiries. It also references a database for providing security information, and responds to users with the latest security information.

[1048] The server uses the Flask framework to build a web application and manages information using an SQLite database. User devices, such as smartphones and PCs, access the server via the Internet. When a user requests specific information, the server retrieves the relevant information from the database and provides it to the user.

[1049] Specifically, when a user makes a query from their device such as "What are the latest phishing scams?", the server retrieves information about the latest phishing scams from the SQLite database and returns that information to the user, allowing the user to quickly obtain the information they need.

[1050] The hardware used is a computer as the server and a smartphone or PC as the user terminal. The software used is Flask (a Python framework) and SQLite (a database).

[1051] As a concrete example, if a user asks, "What are the latest phishing scams?" the server retrieves information about the latest phishing scams from the database and responds as follows:

[1052] "The latest phishing scam is using fake bank emails to steal personal information."

[1053] An example of a prompt for the generative AI model is as follows:

[1054] When a user asks, "What are the latest phishing scams?", retrieve information about the latest phishing scams from your database and respond with the following:

[1055] "The latest phishing scam is using fake bank emails to steal personal information."

[1056] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1057] Step 1:

[1058] The user inputs a query from a device. The user uses a smartphone or computer to input a question that requests specific information. For example, they might input, "What are the latest phishing scams?" The input data is a text query.

[1059] Step 2:

[1060] The terminal sends a query to the server. The query entered by the user is sent to the server via the Internet. The input data is the user's query text, and the output data is the query sent to the server.

[1061] Step 3:

[1062] The server receives the query and accesses the database. The server receives the query and accesses the SQLite database using the Flask framework. The input data is the user's query text and the output data is the result of executing the database query.

[1063] Step 4:

[1064] The server retrieves the relevant information from the database. The server executes an SQL query to retrieve information from the database that corresponds to the user's query. For example, information on "the latest phishing scam techniques." The input data is the SQL query, and the output data is the retrieved information.

[1065] Step 5:

[1066] The server generates a response based on the information it has obtained. The server generates a response to the user based on the information it has obtained. For example, it generates a response such as, "The latest phishing scam is to use fake bank emails to steal personal information." The input data is the obtained information, and the output data is the generated response text.

[1067] Step 6:

[1068] The server generates a response and sends it to the terminal. The server generates a response and sends it to the user's terminal. The input data is the generated response text, and the output data is the response sent to the terminal.

[1069] Step 7:

[1070] The device receives the response from the server and displays it to the user. The user's device displays the response received from the server. For example, it displays "The latest phishing scam uses fake bank emails to steal personal information." The input data is the response text from the server, and the output data is the response displayed to the user.

[1071] Example 3

[1072] 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."

[1073] Conventional infertility treatment information systems faced the challenge of being unable to respond quickly and appropriately to user inquiries. Furthermore, they lacked the means to provide detailed information on specific medical procedures, making it difficult for users to obtain the information they needed in a timely manner. Furthermore, the lack of a chat-style inquiry response function resulted in a poor user experience.

[1074] 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.

[1075] In this invention, the server includes means for receiving a user's inquiry, means for analyzing the inquiry, means for retrieving information from a database based on the analysis results, means for sending a prompt to the generative AI model based on the retrieved information, and means for returning a response from the generative AI model to the user. This enables prompt and appropriate responses to user inquiries. Furthermore, by including means for providing detailed information about specific medical procedures, the user can obtain the information they need in a timely manner. Furthermore, by including a chat-style inquiry response function, the user experience can be improved.

[1076] "Means for receiving inquiries from users" refers to the function that enables the server to receive inquiries sent by users via web browsers or mobile apps.

[1077] The "means for analyzing the inquiry" is a function for analyzing the content of the received inquiry and extracting keywords and intentions.

[1078] "Means for obtaining information from a database" refers to a function for obtaining necessary information from a database based on the analysis results.

[1079] "Means for sending prompt sentences to the generative AI model" is a function for sending prompt sentences generated based on the acquired information to the generative AI model.

[1080] "Means for returning a response from the generative AI model to the user" is a function for returning a response received from the generative AI model to the user.

[1081] The "means for providing information about a specific medical procedure" is a function for providing users with detailed information about a specific medical procedure.

[1082] "Means for responding to user inquiries in chat format" is a function for responding to user inquiries in chat format in real time.

[1083] MODE FOR CARRYING OUT THE INVENTION

[1084] The present invention is a system for quickly and appropriately responding to inquiries from users. A specific embodiment of this system will be described below.

[1085] 1. Generating the system program

[1086] The system program is developed using Python and runs as a web server using the Flask framework. It uses MySQL as the database and a common generative AI model (e.g., GPT-3) as the generative AI model.

[1087] 2. Program Processing

[1088] The server receives a query from the user and analyzes it. For analysis, it uses a text analysis library (e.g., NLTK or spaCy). Based on the analysis results, the server retrieves the necessary information from the database. Based on the retrieved information, the server sends a prompt to the generative AI model and returns the response from the generative AI model to the user.

[1089] 3. Specific Examples

[1090] Consider a case where a user uses a web browser or a mobile app to send a query such as "What are the advantages and disadvantages of two-stage porting?" In this case, the server processes the query as follows:

[1091] 1. A user sends a chat inquiry asking, "What are the advantages and disadvantages of two-stage transplant?"

[1092] 2. The server receives and analyzes this query.

[1093] 3. The server runs an SQL query to retrieve information about the "advantages and disadvantages of two-stage migration" from the MySQL database.

[1094] 4. Based on the information obtained, the following prompt sentence is input into the generative AI model:

[1095] Example prompt: "What are the advantages and disadvantages of a two-stage transplant? The advantages are ____ and the disadvantages are ____."

[1096] 5. A generative AI model (e.g., GPT-3) generates a response based on this prompt.

[1097] 6. The server generates a response and returns it to the user in real time.

[1098] In this way, the system can respond quickly and appropriately to inquiries from users. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] A user submits a query

[1101] A user sends a query in chat format using a web browser or a mobile app. For example, they type "What are the advantages and disadvantages of two-stage porting?" and click the send button. The input is the user's query, and the output is an HTTP request to the server.

[1102] Step 2:

[1103] The server receives the query

[1104] The server uses the Flask framework to receive an HTTP request from a user. The received request contains the user's query. The input is the HTTP request from the user, and the output is the text data of the query.

[1105] Step 3:

[1106] The server parses the query

[1107] The server analyzes the received query using a text analysis library (e.g., NLTK or spaCy). Through the analysis, keywords and intent are extracted. The input is the text data of the query, and the output is the analysis results (keywords and intent).

[1108] Step 4:

[1109] The server retrieves information from the database

[1110] The server retrieves the necessary information from the MySQL database based on the analysis results. For example, to retrieve information about "the advantages and disadvantages of two-stage migration," it executes an SQL query. The input is the analysis results, and the output is the information retrieved from the database.

[1111] Step 5:

[1112] The server sends a prompt to the generative AI model

[1113] The server generates a prompt based on the acquired information and sends it to the generative AI model. For example, it generates a prompt such as, "Please tell me about the advantages and disadvantages of two-stage transplantation. The advantages are XX and the disadvantages are △△." The input is information acquired from the database, and the output is a prompt sent to the generative AI model.

[1114] Step 6:

[1115] A generative AI model generates a response

[1116] The generative AI model generates a response based on the received prompt. For example, it might generate a response like, "The advantages of two-stage transplantation are a high success rate and a low burden on the patient. On the other hand, the disadvantages are that the surgery is complicated and time-consuming." The input is the prompt, and the output is the generated response.

[1117] Step 7:

[1118] The server returns the response to the user

[1119] The server returns the response received from the generative AI model to the user. Specifically, it returns the response as an HTTP response and displays it on the user's screen. The input is the generated response, and the output is the HTTP response to the user.

[1120] (Application example 3)

[1121] 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."

[1122] Conventional infertility treatment information systems have had the problem of being unable to respond quickly and accurately to user inquiries. Furthermore, there was a lack of detailed information on specific infertility treatment methods, which meant it took a long time for users to obtain the information they needed. Furthermore, there was a lack of technology to generate appropriate responses to user questions.

[1123] 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.

[1124] In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information on infertility treatment based on information obtained from the database, a means for responding to inquiries from users based on the information obtained from the database, and a means for generating appropriate responses to user questions using a generative AI model. This allows users to receive prompt and accurate responses, and detailed information on specific infertility treatment methods is also provided, enabling them to quickly obtain the information they need.

[1125] "Past treatment results" refers to data on the results and progress of previous infertility treatments.

[1126] "Statistical information" refers to data that shows the results of compiling and analyzing multiple treatment data.

[1127] "Information disclosure by research institutions" refers to research results and reports on infertility treatment that are made public by academic institutions and medical institutions.

[1128] A "database" is a system that systematically manages and stores data such as past treatment results, statistical information, and information disclosure by research institutions.

[1129] "Information on infertility treatment" refers to detailed data on infertility treatment methods, effects, side effects, success rates, etc.

[1130] "User inquiries" are actions by individuals using the system to ask questions or request information about infertility treatment.

[1131] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate appropriate responses to user questions.

[1132] An "appropriate response" is an answer that provides accurate and useful information to the user's question.

[1133] A system for carrying out this invention is configured as follows: The server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, and means for generating appropriate responses to user questions using a generative AI model.

[1134] Hardware and software used

[1135] Hardware: Servers, smartphones

[1136] Software: Python, SQLite, OpenAI API

[1137] Data processing and calculation

[1138] The server first connects to a database using SQLite, which contains past treatment results, statistical information, and information disclosure by research institutions. When a user accesses the system using a smartphone and makes an inquiry via chat, the server analyzes the content of the inquiry.

[1139] Retrieving information from a database

[1140] If the user's query is about a specific fertility treatment method, the server retrieves the relevant information from the database and provides it to the user. For example, if the user asks, "What are the advantages and disadvantages of two-stage transplantation?", the server retrieves information about two-stage transplantation from the database and generates a response based on that information.

[1141] Using generative AI models

[1142] If the user's query is not directly related to the database, the server uses a generative AI model to generate an appropriate response. Specifically, it uses the OpenAI API to generate a prompt for the user's question, and the AI ​​generates a response based on that prompt.

[1143] Specific examples

[1144] For example, if a user asks, "What are the specs of the iPhone 13?", the server retrieves the specs of the iPhone 13 from the database and returns them to the user. Also, if a user asks, "What are the advantages and disadvantages of two-stage porting?", the server sends the following prompt to the generative AI model:

[1145] User Question: What are the advantages and disadvantages of a two-stage transplant?

[1146] answer:

[1147] Based on this prompt, the generative AI model generates an appropriate response and provides it to the user, allowing them to obtain information quickly and accurately.

[1148] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1149] Step 1:

[1150] Users access the system using their smartphones and make inquiries via chat.

[1151] Input: User question (e.g., "What are the advantages and disadvantages of two-stage transplantation?")

[1152] Output: The user's question is sent to the server.

[1153] Specific actions: A user opens a chat application on their smartphone, types a question, and presses the send button.

[1154] Step 2:

[1155] The server receives the user's question and analyzes its contents.

[1156] Input: User's question

[1157] Output: Analysis result of question (e.g. "Question about two-stage transplant")

[1158] Specific operation: The server analyzes the received text data using a natural language processing algorithm to identify the intent of the question.

[1159] Step 3:

[1160] The server connects to the database and looks up the appropriate information.

[1161] Input: Analysis result of question content

[1162] Output: Information retrieved from the database (e.g., "Information about the advantages and disadvantages of two-stage transplantation")

[1163] What happens next: The server queries the SQLite database and retrieves the relevant information.

[1164] Step 4:

[1165] The server generates a response based on information retrieved from the database.

[1166] Input: Information retrieved from the database

[1167] Output: Response to the user (e.g., "The advantages of two-stage porting are..., the disadvantages are...")

[1168] Specific behavior: The server formats the information it obtains into text and generates a response to send to the user.

[1169] Step 5:

[1170] The server uses the generative AI model to generate appropriate responses to the user's questions.

[1171] Input: User's question

[1172] Output: The response provided by the generative AI model (e.g., "The advantages and disadvantages of two-stage transplantation are...")

[1173] Specific behavior: The server sends a prompt to the OpenAI API and receives the generated response.

[1174] Step 6:

[1175] The server generates a response and sends it to the user.

[1176] Input: Generated response content

[1177] Output: A response message that will be displayed on the user's smartphone.

[1178] Specific behavior: The server sends the generated response to the user through the chat application, which displays it on the user's smartphone.

[1179] 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.

[1180] "Example 1"

[1181] One embodiment of the present invention is an infertility treatment information provision system that incorporates an emotion engine that recognizes the user's emotions. In this system, when a user makes an inquiry to the system, the emotion engine recognizes the user's emotions. The emotion engine analyzes emotions from the user's text input or voice input, and the results are used by the system. For example, if the user expresses anxiety, the system recognizes that emotion and provides information that will alleviate the anxiety.

[1182] "Example 2"

[1183] Another embodiment of the present invention is a system in which an emotion engine provides information about infertility treatment based on a user's emotions. In this system, the emotion engine analyzes the user's emotions and provides information appropriate to those emotions. For example, if the user expresses hopeful emotions, the system provides information about success stories and new treatments.

[1184] "Example 3"

[1185] In another embodiment of the present invention, an emotion engine responds to a user's inquiry based on the user's emotion. In this system, the emotion engine analyzes the user's emotion and generates a response appropriate to the emotion. For example, if the user is depressed, the system generates a response that includes words of encouragement.

[1186] The processing flow of each embodiment will be described below.

[1187] "Example 1"

[1188] Step 1: The user queries the system.

[1189] Step 2: The emotion engine analyzes emotions from the user's text or voice input.

[1190] Step 3: The system uses the analysis results of the emotion engine to provide information that matches the user's emotions.

[1191] "Example 2"

[1192] Step 1: The emotion engine analyzes the user's emotions.

[1193] Step 2: The system uses the analysis results of the emotion engine to provide information that matches the user's emotions.

[1194] "Example 3"

[1195] Step 1: The emotion engine analyzes the user's emotions.

[1196] Step 2: The system uses the analysis results of the emotion engine to generate a response that matches the user's emotions.

[1197] Example 1

[1198] 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."

[1199] Conventional infertility treatment information systems provide information without considering the user's feelings, and therefore are unable to fully alleviate the anxiety and stress felt by users. In addition, the lack of appropriate information based on the user's feelings has led to a problem of reduced user satisfaction.

[1200] 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.

[1201] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, emotion analysis means for recognizing the user's emotions, and means for providing appropriate information based on the emotions recognized by the emotion analysis means. This makes it possible to provide appropriate information according to the user's emotions, reducing user anxiety and stress and improving satisfaction.

[1202] "Past treatment results" refers to data on the results and process of past infertility treatments.

[1203] "Statistical information" is the result of compiling and analyzing data related to infertility treatment.

[1204] "Information disclosure by research institutions" refers to research results and reports on infertility treatment that are made public by research institutions.

[1205] A "database" is a system that organizes and stores information including past treatment results, statistical information, and information disclosure by research institutions.

[1206] "Emotion analysis means" is a technology that analyzes emotions from a user's text or voice input.

[1207] "Means for providing appropriate information" refers to technology that selects and provides information that is useful to the user based on the results of analyzing the user's emotions.

[1208] "Means for responding to inquiries" refers to technology that provides answers to questions and requests from users based on information retrieved from a database.

[1209] The present invention is a system for providing information on infertility treatment, which provides appropriate information to users by referencing a database containing past treatment results, statistical information, and information disclosure by research institutions. Furthermore, it has the function of recognizing the user's emotions and providing information based on those emotions.

[1210] Hardware and software used

[1211] 1. Server

[1212] The server uses a database management system (e.g., MySQL, PostgreSQL) to manage data including past treatment results, statistical information, and information disclosure at research institutions.

[1213] The server uses web server software (e.g., Apache, Nginx) to receive and process HTTP requests from users.

[1214] 2. Emotion analysis method

[1215] As a means of sentiment analysis, we use natural language processing APIs (e.g., Google Cloud Natural Language API, IBM Watson Tone Analyzer) to analyze emotions from user text and voice input.

[1216] 3. Terminal

[1217] Users access the system using a web browser (e.g., Google Chrome, Safari) or a mobile app.

[1218] Data processing and calculation

[1219] 1. Database Reference

[1220] The server uses a database management system to access data including past treatment results, statistical information, and information disclosures from research institutions, thereby obtaining basic data to provide appropriate information in response to user inquiries.

[1221] 2. Emotion analysis

[1222] The server sends the user's text or voice input to the emotion analysis means, which analyzes the user's emotions. The analysis results are returned to the server in JSON format.

[1223] 3. Information provision

[1224] The server searches for appropriate information from the database based on the emotion analysis results and provides it to the user. For example, if the user expresses anxiety, it provides information on success stories and relaxation methods to alleviate anxiety.

[1225] Specific examples

[1226] For example, if a user enters the text "I'm worried because my recent treatment hasn't gone well," the process would proceed as follows:

[1227] 1. A user accesses the system using Google Chrome.

[1228] 2. The user types "I'm worried because my recent treatment wasn't going well" into the text box.

[1229] 3. The server receives this input through Apache.

[1230] 4. The server calls the Google Cloud Natural Language API and sends the input data.

[1231] 5. The emotion analysis means analyzes the emotion of "anxiety."

[1232] 6. The server receives the parsed results in JSON format.

[1233] 7. The server references the MySQL database.

[1234] 8. Search for information on success stories and relaxation techniques to help your server reduce anxiety.

[1235] 9. The server displays the search results on a web page and serves them to the user.

[1236] Prompt Sentence Examples

[1237] Examples of prompts to input to a generative AI model include:

[1238] If a user types, "I'm worried because my recent treatment didn't go well," explain how the emotion engine will respond and what information the server will provide.

[1239] Using this prompt, the generative AI model can recognize the user's emotions and understand the system's behavior to provide appropriate information.

[1240] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1241] Step 1:

[1242] A user accesses the system.

[1243] Users access the system using a web browser or a mobile app, for example, using a browser such as Google Chrome or Safari. The input is the user's access request, and the output is the system homepage being displayed.

[1244] Step 2:

[1245] The user enters a query.

[1246] The user enters a query using a text box or voice input function, for example, "I'm worried because my recent treatment didn't go well." The input is the user's text or voice data, and the output is that data being sent to the server.

[1247] Step 3:

[1248] The server receives the user's input.

[1249] The server receives an HTTP request and gets the user's input data. For example, we use a web server such as Apache or Nginx. The input is the user's query data, and the output is that data ready to be processed within the server.

[1250] Step 4:

[1251] The server invokes the emotion engine.

[1252] The server calls the emotion engine's API and sends the user's input data, for example, using Google Cloud Natural Language API or IBM Watson Tone Analyzer. The input is the user's text or voice data, and the output is the emotion analysis result.

[1253] Step 5:

[1254] The emotion engine analyzes the user's emotions.

[1255] The emotion engine analyzes the user's text or voice input to identify emotions, for example, recognizing the emotion "anxiety." The input is the user's text or voice data, and the output is the analyzed emotion data.

[1256] Step 6:

[1257] The server receives the analysis results.

[1258] The server receives the analysis results from the emotion engine and proceeds to the next step. For example, it receives the results in JSON format. The input is the emotion analysis results, and the output is that the results are available within the server.

[1259] Step 7:

[1260] The server references the database.

[1261] The server uses a database management system such as MySQL or PostgreSQL to access the database. The input is the sentiment analysis results and the user's query, and the output is the related information retrieved from the database.

[1262] Step 8:

[1263] The server looks up the appropriate information.

[1264] The server retrieves appropriate information from a database based on the user's emotions, for example, information about success stories and relaxation techniques to alleviate anxiety. The input is the information retrieved from the database, and the output is appropriate information to provide to the user.

[1265] Step 9:

[1266] The server provides the search results to the user.

[1267] The server returns the search results to the user, for example by displaying the results on a web page or by reading the results aloud. The input is the relevant information, and the output is the information presented to the user.

[1268] (Application example 1)

[1269] 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."

[1270] Infertility treatment information provision systems, the lack of information provided takes user emotions into consideration, which can cause users to feel anxious or stressed. Furthermore, conventional systems do not provide information in a virtual space, making it difficult for users to obtain information intuitively.

[1271] 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.

[1272] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information about infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, means for recognizing the user's emotions using an emotion engine that analyzes the user's emotions, means for providing appropriate information according to the recognized emotions, and means for providing an interface for viewing information in a virtual space. This makes it possible to provide appropriate information according to the user's emotions, allowing the user to intuitively obtain information in the virtual space.

[1273] A "database" is a system that organizes and stores information, including past treatment results, statistical information, and information disclosure by research institutions, and can be referenced as needed.

[1274] The "means for providing information about infertility treatment" is a function that provides users with information about infertility treatment based on information obtained from the database.

[1275] The "inquiry response means" is a function that responds to inquiries from users based on information obtained from the database.

[1276] An "emotion engine" is a technology that analyzes emotions from a user's text or voice input and makes the results available to the system.

[1277] The "emotion recognition means" is a function that recognizes the user's emotions using an emotion engine.

[1278] "Emotion response means" is a function that provides appropriate information according to the recognized emotion.

[1279] A "virtual interface" is an interface that allows a user to view information within a virtual space.

[1280] A system for implementing the present invention has the following configuration.

[1281] System configuration

[1282] 1. Database reference method

[1283] The server accesses a database containing past treatment results, statistical information, and information disclosures from research institutions. This database organizes and stores a variety of information related to infertility treatment, allowing for quick access when needed.

[1284] 2. Means of providing information on infertility treatment

[1285] The server uses the information retrieved from the database to provide users with information about fertility treatments, based on the specific treatments and statistical data they require.

[1286] 3. Inquiry response methods

[1287] The server responds to user inquiries based on information retrieved from the database, allowing users to obtain the information they need in real time.

[1288] 4. Emotion recognition means

[1289] The server recognizes the user's emotions using an emotion engine, which analyzes emotions from the user's text and voice input and makes the results available to the system.

[1290] 5. Emotional response measures

[1291] The server provides appropriate information depending on the emotion it recognizes. For example, if the user is feeling anxious, it provides reassuring information.

[1292] 6. Virtual Interfaces

[1293] The terminal provides an interface for users to view information in the virtual space, allowing users to obtain information intuitively.

[1294] Hardware and software used

[1295] Hardware: Smartphone, head-mounted display

[1296] Software: Python, emotion recognition library (EmotionEngine), database management system (TreatmentDatabase)

[1297] Data processing and calculation

[1298] 1. Getting User Input

[1299] The device receives text or voice input from the user.

[1300] 2. Emotion analysis

[1301] The server uses an emotion engine to analyze emotions from the user's input.

[1302] 3. Information provision

[1303] Based on the results of the sentiment analysis, the server retrieves appropriate information from the database and provides it to the user.

[1304] Specific examples

[1305] If a user types, "I'm worried about the results of my recent treatment," the emotion engine will interpret this as "anxiety," and the server will provide reassuring information.

[1306] If the user types, "I want to know about the latest treatments," the server provides general treatment information.

[1307] Prompt Sentence Examples

[1308] If a user types, "I'm worried about my recent treatment results," the emotion engine should interpret this as "anxiety" and provide reassuring information.

[1309] In this way, it becomes possible to provide appropriate information according to the user's emotions, realizing a system that allows users to obtain information intuitively within a virtual space.

[1310] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1311] Step 1:

[1312] The user inputs a query into the terminal by text or voice.

[1313] Input: User text or voice input (e.g., "I'm worried about my recent medical results")

[1314] Output: User input data

[1315] Specific operation: The device receives user input and stores it as text data or voice data.

[1316] Step 2:

[1317] The terminal transmits the user's input data to the server.

[1318] Input: User-entered data

[1319] Output: User input data sent to the server

[1320] Specific operation: The terminal transmits the user's input data to the server via the network.

[1321] Step 3:

[1322] The server uses an emotion engine to analyze emotions from the user's input data.

[1323] Input: User-entered data

[1324] Output: User's emotion data (e.g., "anxiety")

[1325] Specific operation: The server starts the emotion engine and analyzes the user's input data to identify emotions. The emotion engine performs text and voice analysis to classify the user's emotions into categories such as "anxiety," "relief," and "interest."

[1326] Step 4:

[1327] The server retrieves appropriate information from a database based on the emotion data.

[1328] Input: User emotion data (e.g., "anxiety")

[1329] Output: Appropriate information data (e.g., "reassuring information")

[1330] Specific operation: The server searches the database based on the emotion data and retrieves information corresponding to the user's emotion. For example, if the analysis finds that the emotion is "anxiety," it extracts information to reassure the user from the database.

[1331] Step 5:

[1332] The server transmits the acquired information to the terminal.

[1333] Input: Appropriate information data

[1334] Output: Information data sent to the terminal

[1335] Specific operation: The server transmits the acquired information data to the terminal via the network.

[1336] Step 6:

[1337] The terminal displays the received information to the user.

[1338] Input: Received information data

[1339] Output: Information displayed to the user

[1340] Specific operation: The terminal displays the received information data to the user. The display method can be a text display, a voice readout, or a display using a virtual interface.

[1341] In this way, it becomes possible to provide appropriate information according to the user's emotions, and a system is realized that allows users to obtain information intuitively within a virtual space.

[1342] Example 2

[1343] 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."

[1344] Conventional infertility treatment information systems can sometimes have difficulty responding appropriately and quickly to user inquiries. Furthermore, because information is not provided in response to users' emotions, user satisfaction may decline. Furthermore, because there is a lack of means to provide detailed information about specific infertility treatment methods, users may not be able to obtain the information they need.

[1345] 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.

[1346] In this invention, the server includes a means for receiving inquiries from users, a means for analyzing the received inquiries, a means for retrieving information from a database, a means for providing the retrieved information to the user, and a means for analyzing the user's emotions using an emotion engine and providing information appropriate to those emotions. This enables a quick and appropriate response to user inquiries, and provides information according to the user's emotions, thereby improving user satisfaction. In addition, detailed information on specific infertility treatment methods can be provided, allowing users to obtain sufficient information they need.

[1347] "Means for receiving inquiries from users" refers to an interface through which the system receives questions or requests entered by users.

[1348] "Means for analyzing the content of received inquiries" refers to a function for understanding received user inquiries using technologies such as natural language processing and extracting appropriate information.

[1349] The "means for acquiring information from a database" is a function for searching and acquiring related information from a database based on the analyzed query content.

[1350] The "means of providing the retrieved information to the user" is an interface that displays the information retrieved from the database in an easy-to-understand manner for the user.

[1351] "Means of analyzing a user's emotions using an emotion engine and providing information appropriate to those emotions" is a function that uses technology to analyze a user's emotions to select and provide information that corresponds to the user's emotional state.

[1352] "Means for providing information on specific infertility treatment methods" is a function for providing users with detailed information on specific infertility treatment methods, such as PGTA and two-stage transplantation.

[1353] The "means for responding to inquiries from users in a chat format" is an interface for conducting dialogue with users in a chat format and responding to inquiries in real time.

[1354] MODE FOR CARRYING OUT THE INVENTION

[1355] This invention is a system that responds to user inquiries based on information retrieved from a database. Specifically, when a user inquires about a specific infertility treatment method, the system retrieves information about the corresponding treatment method from the database and responds to the user based on that information. It also includes a function that uses an emotion engine to analyze the user's emotions and provide information appropriate to those emotions.

[1356] Hardware and software used

[1357] The server uses the following hardware and software:

[1358] Hardware: High-performance server machine (e.g., Intel Xeon processor, 32GB RAM, 1TB SSD)

[1359] software:

[1360] Database management system (e.g. MySQL)

[1361] Natural language processing engines (e.g., spaCy, NLTK)

[1362] Sentiment analysis engine (e.g. IBM Watson sentiment analysis API)

[1363] A terminal is a device through which a user inputs an inquiry and receives a response from the system. Terminals can be PCs, smartphones, tablets, etc.

[1364] Program processing explanation

[1365] When the server receives a user's inquiry, it analyzes the inquiry using a natural language processing engine. Based on the analysis results, the server retrieves the relevant information from the MySQL database. The retrieved information is sent to the terminal and displayed to the user.

[1366] Furthermore, the server analyzes the user's emotions using an emotion analysis engine. Based on the analysis results, it selects information appropriate to the user's emotions and sends it to the device. The device then displays the selected information to the user.

[1367] Specific examples

[1368] When a user asks "What is the success rate of PGTA?", the following steps are taken:

[1369] 1. The terminal receives a user query.

[1370] 2. The server uses a natural language processing engine to extract the keywords "PGTA" and "success rate."

[1371] 3. The server connects to the MySQL database and executes the following SQL query:

[1372] sql

[1373] SELECT success_rate FROM fertility_treatments WHERE treatment_name = 'PGTA';

[1374] 4. The server sends the acquired success rate data to the terminal.

[1375] 5. The terminal will display to the user, "PGTA success rate is 70%."

[1376] If the user expresses hopeful emotions, the server uses an emotion analysis engine to select information about success stories and new treatments based on the analysis results and sends it to the device, which then displays, "Recent research has led to the development of new treatments."

[1377] Prompt Sentence Examples

[1378] Below are some examples of prompts for generative AI models:

[1379] When a user asks, "What is the success rate of PGTA?", generate a program that retrieves information about the success rate of PGTA from the database and provides it to the user.

[1380]

[1381] If the user is expressing hopeful emotions, use the emotion engine to generate programs that provide information such as success stories and new treatments.

[1382] The above is an embodiment of the present invention.

[1383] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1384] Program processing flow

[1385] Step 1: Receive a user inquiry

[1386] The terminal receives a query from the user. For example, the user enters "What is the success rate of PGTA?" The entered query is sent from the terminal to the server.

[1387] Input: User query (e.g. "What is the success rate of PGTA?")

[1388] Output: Query data sent to the server

[1389] Step 2: Analyze the query

[1390] The server analyzes the received query using a natural language processing engine. Specifically, it uses NLP tools such as spaCy and NLTK to understand the intent of the query and extract important keywords.

[1391] Input: Inquiry data sent from the terminal

[1392] Data processing: Extract keywords using a natural language processing engine (e.g., "PGTA", "success rate")

[1393] Output: Extracted keywords

[1394] Step 3: Retrieving information from the database

[1395] The server retrieves the relevant information from the MySQL database based on the analysis results. For example, it executes an SQL query to retrieve information about the "PGTA success rate."

[1396] Input: Extracted keywords (e.g., "PGTA", "success rate")

[1397] Data Calculation: Execute an SQL query to retrieve information from the database (e.g. SELECT success_rate FROM fertility_treatments WHERE treatment_name = 'PGTA';)

[1398] Output: Retrieved information (e.g. "PGTA success rate is 70%")

[1399] Step 4: Provide information to users

[1400] The server sends the acquired information to the terminal, which then displays the received information to the user. For example, it displays "PGTA success rate is 70%."

[1401] Input: Retrieved information (e.g. "PGTA success rate is 70%")

[1402] Output: Information displayed to the user

[1403] Step 5: Provide information using the emotion engine (if necessary)

[1404] The server uses an emotion engine to analyze the user's emotions. For example, it uses IBM Watson's emotion analysis API. If the user expresses hopeful emotions, the server selects information about success stories and new treatments and sends it to the device. The device then displays the selected information to the user.

[1405] Input: User emotion data

[1406] Data processing: Analyze emotions using a sentiment analysis engine and select appropriate information

[1407] Output: Emotionally appropriate information displayed to the user (e.g., "Recent research has led to the development of a new treatment")

[1408] The above is the flow of processing of the program of this system.

[1409] (Application example 2)

[1410] 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."

[1411] There is a need for information provision systems related to infertility treatment to provide appropriate information according to the user's emotions. However, conventional systems provide uniform information without considering the user's emotions, making it difficult to reduce the psychological burden on the user.

[1412] 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.

[1413] In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information on infertility treatment based on information obtained from the database, a means for responding to inquiries from users based on the information obtained from the database, a means for analyzing the user's emotions, and a means for providing appropriate information based on the emotion analysis means. This makes it possible to provide appropriate information according to the user's emotions, thereby reducing the psychological burden on the user.

[1414] The "system for providing information on infertility treatment" refers to a database containing past treatment results, statistical information, and information disclosure by research institutions, and provides information on appropriate infertility treatment in response to user inquiries.

[1415] "Means for referencing databases" refers to means for obtaining necessary information from databases that include past treatment results, statistical information, and information disclosure by research institutions.

[1416] "Means for providing information on infertility treatment" refers to means for providing appropriate information on infertility treatment to users based on information obtained from the database.

[1417] "Means for responding to inquiries from users" refers to means for responding based on information obtained from the database when a user inquires about a specific infertility treatment method.

[1418] "Means for analyzing user emotions" refers to means for analyzing the user's facial expressions, tone of voice, etc. to determine the user's emotions.

[1419] The "means for providing appropriate information based on emotion analysis means" refers to a means for providing information on infertility treatment that is appropriate for the user's emotions based on the results of the user's emotion analysis.

[1420] This invention is a system for providing information on infertility treatment, which refers to a database containing past treatment results, statistical information, and information disclosure by research institutions, and provides appropriate information on infertility treatment in response to user inquiries. It also has the function of analyzing the user's emotions and providing appropriate information based on those emotions.

[1421] System configuration

[1422] The system consists of the following major components:

[1423] 1. Database reference method:

[1424] The server accesses a database containing a variety of information related to infertility treatment, including past treatment results, statistical information, and information disclosure by research institutions.

[1425] 2. Information provision method:

[1426] The server provides users with information about infertility treatments based on the information retrieved from the database, allowing users to learn about the latest treatments and success rates.

[1427] 3. Query response methods:

[1428] The server responds to user inquiries based on information retrieved from the database. For example, if a user asks, "What is the success rate of PGTA?", the server retrieves the relevant information from the database and provides it to the user.

[1429] 4. Emotion analysis means:

[1430] The server analyzes the user's facial expressions and tone of voice to determine their emotions, using the camera and microphone installed in the smart glasses.

[1431] 5. Emotion-based information delivery methods:

[1432] The server provides information appropriate to the user's emotions based on the emotion analysis method, for example, if the user expresses hopeful emotions, it provides information about success stories and new treatments.

[1433] Hardware and software used

[1434] Hardware:

[1435] Smart glasses (with camera and microphone)

[1436] server

[1437] software:

[1438] Python

[1439] OpenCV (image processing library)

[1440] EmotionRecognizer (emotion recognition library)

[1441] Database (database access library)

[1442] Processing flow

[1443] The server uses the smart glasses' camera and microphone to capture the user's facial expressions and voice. Using the captured data, the emotion recognition model analyzes the user's emotions. It then receives the user's inquiry and retrieves relevant information from the database. Based on the emotion analysis results, it provides additional information (such as success stories or new treatments). Finally, the captured information is displayed on the smart glasses.

[1444] Specific examples

[1445] For example, if a patient asks, "What is the success rate of PGTA?" and expresses hopeful sentiment, the smart glasses will display, "PGTA has a 70% success rate. We will also share recent success stories and new treatments."

[1446] Prompt Sentence Examples

[1447] Develop a smart glasses application that analyzes the patient's facial expressions and voice and provides information about fertility treatments based on their emotions. When a patient asks, "What is the success rate of PGTA?", retrieve information from a database and, if the patient expresses hopeful emotions, provide information about success stories and new treatments.

[1448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1449] Step 1:

[1450] The server uses the camera and microphone of the smart glasses to capture the user's facial expressions and voice. The input is the user's facial image and voice data, and the output is these data. Specifically, the camera takes a picture of the user's face and the microphone records their voice.

[1451] Step 2:

[1452] The server runs an emotion recognition model using the acquired facial image and audio data. The input is the facial image and audio data acquired in step 1, and the output is the user's emotion (e.g., hopeful, pessimistic, etc.). Specifically, it uses the EmotionRecognizer library to analyze emotions from images and audio.

[1453] Step 3:

[1454] The server receives the user's query. The input is the user's voice or text, and the output is the query content. Specifically, it uses voice recognition technology to convert the user's voice into text and extracts the query content.

[1455] Step 4:

[1456] The server retrieves the relevant information from the database. The input is the inquiry retrieved in step 3, and the output is information about the relevant infertility treatment. Specifically, the server uses the Database library to search the database for information corresponding to the inquiry.

[1457] Step 5:

[1458] The server selects additional information based on the emotion analysis results. The input is the emotion data obtained in step 2 and the infertility treatment information obtained in step 4, and the output is additional information appropriate to the emotion. Specifically, if the emotion is hopeful, it selects information about success stories and new treatments.

[1459] Step 6:

[1460] The server displays the final information on the smart glasses. The input is the information selected in step 5, and the output is the information displayed on the smart glasses display. Specifically, the acquired information is displayed in text format on the smart glasses display.

[1461] Step 7:

[1462] The user reviews the information provided through the smart glasses. The input is the information displayed on the smart glasses' display, and the output is the user's understanding and next action. Specifically, the user reads the displayed information and considers the next treatment step or question.

[1463] Example 3

[1464] 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."

[1465] Conventional infertility treatment information systems struggled to provide prompt and appropriate responses to user inquiries. They also lacked the ability to generate responses based on the user's emotions, leaving the user experience open to improvement. Furthermore, they lacked detailed information about specific infertility treatment methods.

[1466] 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.

[1467] In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information on infertility treatment based on information obtained from the database, a means for responding to user inquiries based on the information obtained from the database, a means for generating responses in natural language using a generative AI model, and a means for analyzing user emotions and generating responses appropriate to those emotions. This makes it possible to provide quick and appropriate responses to user inquiries, and by generating responses appropriate to the user's emotions, the user experience can be improved.

[1468] A "database" is a collection of information including past treatment results, statistical information, and information disclosure by research institutions.

[1469] "Means for providing information about infertility treatment" is a function that provides information about infertility treatment to users based on information obtained from the database.

[1470] "Means for responding to inquiries from users" refers to a function that returns a response to an inquiry entered by a user based on information retrieved from a database.

[1471] A "generative AI model" is an artificial intelligence model used to generate natural language responses.

[1472] "Means for generating natural language responses" refers to the ability to use a generative AI model to generate appropriate responses in natural language to user inquiries.

[1473] The "means for analyzing emotions" is a function that analyzes the user's emotions and generates an appropriate response based on those emotions.

[1474] "Means of responding in chat format" refers to a function that provides a real-time response to user inquiries via a chat interface.

[1475] MODE FOR CARRYING OUT THE INVENTION

[1476] This invention is an infertility treatment information system that provides real-time responses to user inquiries. The system references a database containing past treatment results, statistical information, and information disclosures from research institutions, and generates responses based on the information obtained. It also uses a generative AI model to generate natural language responses and analyzes user sentiment to provide appropriate responses.

[1477] Hardware and software used

[1478] server

[1479] The server accesses the database and receives and processes user queries. The server uses the following software:

[1480] Database Management System (DBMS)

[1481] Generative AI models (e.g., GPT-4)

[1482] Sentiment analysis software (e.g., IBM Watson's Tone Analyzer)

[1483] Terminal

[1484] A terminal is a device through which a user enters queries and receives and displays responses from the server. The terminal uses the following software:

[1485] Chat Interface

[1486] Data processing and calculation

[1487] Database Access

[1488] When the server receives a query from a user, it accesses the database to retrieve relevant information. For example, in response to the query "What are the advantages and disadvantages of two-stage transplantation?", the server searches the database for information about two-stage transplantation and retrieves the information about the advantages and disadvantages.

[1489] Response generation using generative AI models

[1490] The server generates a natural language response using a generative AI model (GPT-4) based on the acquired information. The generative AI model receives the query content and acquired information as input and generates an appropriate response.

[1491] Emotion analysis

[1492] The server uses emotion analysis software to analyze the user's emotions. For example, if the user is feeling down, the emotion analysis software will detect that emotion and generate a response that includes encouraging words.

[1493] Specific examples

[1494] Example 1: Inquiry about the advantages and disadvantages of two-stage transplantation

[1495] 1. A user types into the chat, "What are the advantages and disadvantages of a two-stage transplant?"

[1496] 2. The device sends this query to the server.

[1497] 3. The server receives the query and retrieves information about the two-stage migration from the database.

[1498] 4. Based on the information obtained by the server, a generative AI model (GPT-4) is used to generate a response.

[1499] 5. The server generates a response and sends it to the terminal.

[1500] 6. The device displays the following to the user: "The advantage of a two-stage transplant is that it has a higher success rate. The disadvantage is that the surgery is more complicated."

[1501] Example 2: Responding using the Emotion Engine

[1502] 1. A user types into the chat, "I've been feeling down lately because things haven't been going well at work."

[1503] 2. The device sends this query to the server.

[1504] 3. The server receives the query and the emotion analysis software analyzes the user's emotion.

[1505] 4. Sentiment analysis software detects the user's disappointment and generates a response with encouraging words.

[1506] 5. The server generates a response and sends it to the terminal.

[1507] 6. The device displays a message to the user saying, "That's tough, but you've overcome many challenges before. I'm sure you can get through this too."

[1508] In this way, the system can provide a quick and appropriate response to the user's inquiry and generate a response according to the user's emotion, thereby improving the user experience. The flow of the identification process in Example 3 will be described with reference to FIG.

[1509] Step 1:

[1510] The user inputs an inquiry in a chat format.

[1511] Specific operation: The user uses the chat interface on the device to input a question or inquiry, for example, "What are the advantages and disadvantages of two-stage transplantation?"

[1512] Input: User's inquiry

[1513] Output: Data sent to the terminal containing the query content

[1514] Step 2:

[1515] The terminal sends a query to the server.

[1516] Specific operation: The terminal sends the query entered by the user to the server in an appropriate format.

[1517] Input: User's inquiry

[1518] Output: The data sent to the server based on the query.

[1519] Step 3:

[1520] The server receives the query and accesses a database to retrieve the relevant information.

[1521] Specific operation: The server receives the query sent from the terminal and accesses the database to retrieve relevant information, such as the advantages and disadvantages of "two-stage transplantation."

[1522] Input: User's inquiry

[1523] Output: Relevant information retrieved from the database

[1524] Step 4:

[1525] The server generates a response using a generative AI model based on the information it obtains.

[1526] Specific operation: Based on the acquired information, the server generates a natural language response using a generative AI model (e.g., GPT-4). The generative AI model receives the query content and acquired information as input and generates an appropriate response.

[1527] Input: Related information obtained from the database, user inquiry

[1528] Output: The natural language response generated by the generative AI model

[1529] Step 5:

[1530] The server generates a response and sends it to the terminal.

[1531] Specific operation: The server sends the response generated by the generative AI model to the device in a format that is easy for the user to understand.

[1532] Input: A natural language response generated by a generative AI model

[1533] Output: Response data sent to the device

[1534] Step 6:

[1535] The terminal displays the response to the user.

[1536] Specific operation: The terminal displays the response received from the server to the user, and the user can check the response from the server through the chat interface.

[1537] Input: Response data sent from the server

[1538] Output: The response that is displayed to the user

[1539] Step 7:

[1540] The emotion engine analyzes the user's emotions and generates emotionally appropriate responses as needed.

[1541] What it does: The emotion engine analyzes the user's emotions. For example, if the user is feeling down, the emotion engine detects that emotion and generates a response that includes encouraging words. This emotion analysis is performed using emotion analysis software (e.g., IBM Watson's Tone Analyzer).

[1542] Input: User emotion data

[1543] Output: Emotionally appropriate response data

[1544] (Application example 3)

[1545] 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."

[1546] Conventional infertility treatment information systems can provide appropriate information in response to user inquiries, but they are unable to generate responses that reflect the user's emotions, which means they are unable to fully resolve the user's anxieties and doubts. Security services also need to provide appropriate support that reflects the user's emotions, but current systems have difficulty achieving this.

[1547] 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 referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information about infertility treatment based on information obtained from the database, means for responding to inquiries from users based on information obtained from the database, and means including an emotion engine for analyzing the user's emotions and generating a response appropriate to those emotions. This makes it possible to provide appropriate information and support according to the user's emotions.

[1548] A "database" is a system that stores information, including past treatment results, statistical information, and information disclosure by research institutions, and can be referenced as needed.

[1549] "Information provision means" is a function for providing users with information about infertility treatment based on information obtained from the database.

[1550] The "response means" is a function for generating a response to a user's inquiry based on information obtained from the database and sending it back to the user.

[1551] An "emotion engine" is a system that analyzes a user's emotions and generates a response appropriate to those emotions.

[1552] "Chat format" is a form of communication in which users make text-based inquiries and receive responses in real time.

[1553] The system for implementing this invention mainly consists of a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosures from research institutions, and provides appropriate information in response to user inquiries. It also uses an emotion engine to analyze the user's emotions and generate a response appropriate to those emotions.

[1554] Hardware and software used

[1555] Hardware: Servers, user devices (smartphones, tablets, PCs, etc.)

[1556] Software: Python, OpenAI API, EmotionRecognizer library, SecurityDatabase class

[1557] Data processing and calculation

[1558] When the server receives a user query, it first analyzes the query, retrieves relevant information from the database, and uses the EmotionRecognizer library to analyze the user's emotions. Based on the results of the emotion analysis, it generates an appropriate response using the OpenAI API.

[1559] Specific examples

[1560] For example, if a user enters "my home security camera isn't working," the server will process the following steps:

[1561] 1. Receive user input.

[1562] 2. Retrieve information about "Security Camera Troubleshooting" from the database.

[1563] 3. Analyze user emotions using the EmotionRecognizer library.

[1564] 4. If the user is concerned, use the OpenAI API to generate a response such as "Don't worry. Please check the power of your camera and restart it. If the problem persists, please contact support."

[1565] Prompt Sentence Examples

[1566] If the user types "My home security camera is not working," the following is an example of a prompt:

[1567] user_input = "My home security camera isn't working"

[1568] response = generate_response(user_input)

[1569] print(response)

[1570] In this way, it becomes possible to provide appropriate information and support according to the user's emotions.

[1571] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1572] Step 1:

[1573] A user uses a terminal to input a security query, for example, "The security camera in my house is not working." This input is sent to the server.

[1574] Input: User query (e.g., "My home security camera isn't working.")

[1575] Output: User query sent to server

[1576] Step 2:

[1577] The server analyzes the received user inquiry. Specifically, it performs text analysis of the inquiry content and extracts related keywords.

[1578] Input: User query

[1579] Output: Extracted keywords (e.g. "security camera", "not working")

[1580] Step 3:

[1581] The server searches the database based on the extracted keywords to obtain related information, for example, information on "security camera troubleshooting."

[1582] Input: Extracted keywords

[1583] Output: Relevant information retrieved from the database (e.g. "Please check the camera's power and restart it")

[1584] Step 4:

[1585] The server uses the EmotionRecognizer library to analyze emotions from the user's query, for example, to determine if the user is feeling anxious.

[1586] Input: User query

[1587] Output: Parsed user emotion (e.g. "anxiety")

[1588] Step 5:

[1589] The server uses the OpenAI API to generate an appropriate response based on the analyzed emotions, for example, if the user is feeling anxious, it generates a reassuring response.

[1590] Input: relevant information retrieved from the database, analyzed user sentiment

[1591] Output: The response generated (e.g. "Don't worry. Please check the power supply of your camera and reboot it. If the problem persists, please contact support.")

[1592] Step 6:

[1593] The server generates a response and sends it to the user's terminal, where the user can view the response.

[1594] Input: Generated response

[1595] Output: The response sent to the user's terminal

[1596] In this way, it becomes possible to provide appropriate information and support according to the user's emotions.

[1597] 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.

[1598] The data generation model 58 is a so-called generative AI (Artificial Intelligence).

[1599] An example of a data generation model58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generative AIs. Data generation models 58

[1600] , 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 the data format of voice data, text data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1601] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1602] 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.

[1603] [Third embodiment]

[1604] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1605] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1606] 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).

[1607] 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.

[1608] 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.

[1609] 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).

[1610] 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.

[1611] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1612] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1613] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1614] do.

[1615] 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.

[1616] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1617] "Example 1"

[1618] One embodiment of the present invention is a system for providing information on infertility treatment. This system has a means for referencing a database containing past treatment results, statistical information, and information disclosed by research institutions. Specifically, the database collects and organizes information on various infertility treatment methods, information on the treatment results and treatment process of patients who have received these treatment methods, and research results on infertility treatment published by various research institutions.

[1619] "Example 2"

[1620] The system has a means for responding to user inquiries based on information retrieved from the database. Specifically, when a user inquires about a specific infertility treatment method, the system retrieves information about the corresponding treatment method from the database and responds to the user based on that information. For example, when a user inquires, "What is the success rate of PGTA?", the system retrieves information about the success rate of PGTA from the database and provides it to the user.

[1621] "Example 3"

[1622] Furthermore, the system has a means for responding to inquiries from users in chat format. Specifically, when a user makes an inquiry in chat format, the system responds in real time. This response is generated based on information retrieved from the database. For example, if a user asks, "What are the advantages and disadvantages of two-stage transplantation?", the system retrieves information about the advantages and disadvantages of two-stage transplantation from the database and responds to the user based on that information.

[1623] The processing flow of each embodiment will be described below.

[1624] "Example 1"

[1625] Step 1: The system consults a database containing past treatment results, statistical information, and information disclosures from research institutions.

[1626] Step 2: Provide information about infertility treatment based on the information obtained from the database. "Example 2"

[1627] Step 1: Accept user inquiries.

[1628] Step 2: Retrieve information corresponding to the query from the database.

[1629] Step 3: Based on the obtained information, return a response to the user.

[1630] "Example 3"

[1631] Step 1: Accept chat-style inquiries from users.

[1632] Step 2: Retrieve information corresponding to the query from the database.

[1633] Step 3: Based on the information obtained, provide a real-time response to the user.

[1634] Example 1

[1635] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1636] Information on infertility treatment is diverse, making it difficult for patients and medical professionals to quickly and accurately obtain the information they need. Furthermore, there is a lack of systems for effectively utilizing past treatment results, statistical information, and information disclosure by research institutions. Furthermore, even if users enter specific search criteria, there are limited ways to provide relevant information. This makes it difficult for users to make decisions about the most appropriate treatment.

[1637] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1638] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, means for organizing collected data by category and storing it in the database, means for analyzing search conditions entered by the user and sending a request to the server, and means for displaying data returned from the server to the user. This allows users to quickly and accurately obtain the necessary infertility treatment information and make decisions to select the most appropriate treatment.

[1639] A "database" is a collection of information that can be stored and referenced, including past treatment results, statistical information, and information disclosure by research institutions.

[1640] A "means of reference" is a method or technique for searching information in a database and obtaining the required data.

[1641] "Means for providing" refers to the methods and techniques for displaying the acquired information to the user and providing it in a usable form.

[1642] "Means of responding" refers to the methods and techniques for returning answers to user inquiries based on information retrieved from a database.

[1643] "Organization means" refers to methods and techniques for classifying collected data into categories and storing them efficiently in a database.

[1644] "Means for parsing" refers to methods or techniques for understanding the search criteria entered by the user and generating an appropriate request based on that.

[1645] "Means for sending" refers to the method or technology for sending the parsed request to the server and obtaining the required information.

[1646] "Display means" refers to a method or technique for displaying data returned from the server in a format that is easy for the user to view.

[1647] This invention is a system for providing information on infertility treatment, and it operates in cooperation with three entities: a server, a terminal, and a user. The specific operation of each entity will be explained below.

[1648] Server Operation

[1649] The server plays a central role in collecting, organizing, and providing information about infertility treatment. The server uses the following hardware and software:

[1650] Hardware: powerful processor, sufficient memory, storage devices

[1651] Software: Database Management System (DBMS), API interface, data analysis tools

[1652] The server automatically collects information about infertility treatments from the internet and affiliated research institutions. For example, it accesses a research institution's database via API to obtain data on the "success rate of treatment method A" or the "side effects of treatment method B." The collected data is organized by category and stored in a database. The data is checked for duplication and inconsistencies, and cleansed as necessary.

[1653] In response to a user request, the server extracts the appropriate information from the database and returns it to the device, filtering the data based on the content of the request to provide the user with the most relevant information.

[1654] Device behavior

[1655] The terminal provides an interface for users to search and view infertility treatment information. The terminal uses the following hardware and software:

[1656] Hardware: Personal computers, smartphones, tablets

[1657] Software: Web browser, mobile application

[1658] The device provides an interface that allows users to easily search for information. For example, it displays a web page or application with a search bar and filtering functions. When the user enters search criteria, the device sends the request to a server. When the request is sent, the device converts the user's input into an appropriate format that the server can understand.

[1659] The device receives the data returned from the server and displays it to the user, providing the displayed information in a visual format such as graphs and charts to help the user intuitively understand the information.

[1660] User Actions

[1661] Users use this system to obtain information about infertility treatment. Users operate the system in the following steps:

[1662] 1. Entering search criteria: The user enters search criteria through the device interface, such as "treatments suitable for women over 40" or "side effects of treatment method D." Filtering options are also available to refine the search criteria.

[1663] 2. Viewing information: The user views the information provided by the server on the device. For example, if the user searches for "success rate of treatment method E," the results are displayed on the device. The user can scroll through the displayed information to check the details, and can also save the information if necessary.

[1664] 3. Decision support: Users consider infertility treatment options based on the information provided. For example, a user might make a decision such as, "I see that treatment method F has a high success rate, so I decide to try that method." Tools are also available to compare multiple treatment methods and find the best option.

[1665] Examples and prompts

[1666] For example, if a user searches for "infertility treatments suitable for women over 35," the process will be as follows:

[1667] 1. A user types "fertility treatments suitable for women over 35" into the device's search bar.

[1668] 2. The device sends the request to the server.

[1669] 3. The server extracts the relevant information from the database and returns it to the terminal.

[1670] 4. The terminal displays the information to the user.

[1671] 5. The user considers the appropriate treatment based on the displayed information.

[1672] By inputting the following prompt sentence into the generative AI model, specific infertility treatment information can be obtained.

[1673] "Please tell me about infertility treatments suitable for women over 35. Please provide detailed information based on past treatment results, statistical information, and information disclosed by research institutions."

[1674] By inputting this prompt, the generative AI model provides relevant information to assist the user in making a decision.

[1675] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1676] Step 1:

[1677] Data collection

[1678] The server automatically collects information about infertility treatments from the internet and affiliated research institutions. As input, it accesses the research institution's database via API to obtain data on the "success rate of treatment method A" and "side effects of treatment method B." As output, the collected data is saved in the server's temporary storage. Specifically, the server periodically schedules data collection and updates the database whenever new information is released.

[1679] Step 2:

[1680] Data organization

[1681] The server organizes the collected data by category and stores it in a database. As input, it receives the data stored in temporary storage. As output, the organized data is stored in the database. Specifically, the server checks for data duplication and inconsistency and cleanses the data as necessary. For example, if data for the same patient is collected multiple times, it will be merged into one.

[1682] Step 3:

[1683] Providing a user interface

[1684] The device provides an interface that allows users to easily search for information. As input, it receives the user's search criteria. As output, the search criteria are sent to the server. Specifically, the device displays a web page or application with a search bar and filtering function. It analyzes the search criteria entered by the user in real time and provides a suggestion function.

[1685] Step 4:

[1686] Submitting a Request

[1687] The terminal sends the search criteria entered by the user to the server. As input, it receives the user's search criteria. As output, it sends a request converted into an appropriate format to the server. In concrete terms, when sending a request, the terminal converts the user's input into an appropriate format so that the server can understand it.

[1688] Step 5:

[1689] Data provision

[1690] In response to a user request, the server extracts the appropriate information from the database and returns it to the terminal. As input, it receives the request sent from the terminal. As output, it returns the relevant information to the terminal. Specifically, the server filters data based on the content of the request and provides the user with the most relevant information.

[1691] Step 6:

[1692] Displaying the results

[1693] The terminal displays the data returned from the server to the user. As input, it receives the data returned from the server. As output, it displays the information in a format that is easy for the user to view. Specifically, the terminal provides the displayed information in a visual format such as graphs or charts, allowing the user to intuitively understand the information.

[1694] Step 7:

[1695] Viewing information

[1696] The user views the information displayed on the device. As input, the user receives the information displayed on the device. As output, the user checks the information and saves it if necessary. Specifically, the user scrolls through the displayed information to check the details, and uses the function to save the information if necessary.

[1697] Step 8:

[1698] Decision support

[1699] Based on the provided information, the user considers options for infertility treatment. As input, the user receives the information displayed on the device. As output, the user makes a decision to select the most appropriate treatment. Specifically, the user uses tools to compare multiple treatment methods and find the best option. For example, a comparison table is used to compare the success rate and side effects of each treatment method.

[1700] (Application example 1)

[1701] 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."

[1702] Couples and individuals considering infertility treatment need to collect and compare a lot of information to find the best treatment and medical institution. However, this information is scattered and difficult to collect efficiently. It is also difficult to obtain information based on the latest research results and treatment results, resulting in a lack of information to help users decide on the appropriate treatment and medical institution. For this reason, there is a need for a system that provides reliable information that is easily accessible to users.

[1703] 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.

[1704] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on information obtained from the database, means for proposing the most appropriate treatment based on the symptoms and desired treatment entered by the user, and means for searching for the most appropriate medical institution based on the user's location information and desired treatment. This enables users to efficiently obtain reliable infertility treatment information and select the most appropriate treatment and medical institution.

[1705] "Past treatment results" refers to data on the results and process of past infertility treatments.

[1706] "Statistical information" refers to information that is compiled and analyzed as numerical data on treatment results and treatment processes.

[1707] "Information disclosure by research institutions" refers to information such as research results and papers on infertility treatment that are made public by research institutions.

[1708] A "database" is a system that organizes and stores information including past treatment results, statistical information, and information disclosure by research institutions.

[1709] "Information on infertility treatment" refers to information such as infertility treatment methods, treatment results, treatment process, and the latest research results.

[1710] A "user inquiry" is a question or request for information made by a user to the system.

[1711] "Symptom" means any physical or medical condition related to infertility that you are experiencing.

[1712] "Desired treatment" refers to the type of infertility treatment the user wishes to undergo.

[1713] The "optimal treatment" is the infertility treatment method that best suits the user's symptoms and wishes.

[1714] "Location information" is data about a user's current location.

[1715] "Medical institutions" are facilities such as clinics and hospitals that provide infertility treatment.

[1716] "Chat format" refers to a format in which communication is conducted in real time using text messages.

[1717] A system for implementing this invention includes a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosure from research institutions, and provides information about infertility treatment to users. The user terminal is a device such as a smartphone or tablet, and provides an interface for users to access the system.

[1718] The server includes the following means:

[1719] 1. Database reference method: Refer to databases containing past treatment results, statistical information, and information disclosure by research institutions.

[1720] 2. Information provision method: Provide information about infertility treatment based on information obtained from the database.

[1721] 3. Inquiry response means: Responds to user inquiries based on information retrieved from the database.

[1722] 4. Treatment suggestion method: Suggests the most appropriate treatment based on the symptoms and desired treatment entered by the user.

[1723] 5. Medical institution search tool: Search for the most suitable medical institution based on the user's location and desired treatment.

[1724] Hardware and software used:

[1725] Hardware: Servers, smartphones, tablets

[1726] Software: Flask (a Python web framework), requests (a Python library for sending HTTP requests)

[1727] Data processing and calculation:

[1728] The server receives input data from the user (symptoms, desired treatment, location information) and searches the database for relevant information. The search results suggest the most suitable treatment and medical institution to the user. Specifically, the web application is built using Flask, and communication with the database is performed using the requests library.

[1729] Examples:

[1730] When a user opens the "Infertility Treatment Navigator" app and enters their symptoms (e.g., polycystic ovary syndrome) and desired treatment (e.g., in vitro fertilization), the server searches the database for the most appropriate treatment and medical institution and makes suggestions to the user.

[1731] Example prompt sentence:

[1732] A user has symptoms of Polycystic Ovarian Syndrome and wants to undergo IVF. Please suggest the best treatment and medical institution.

[1733] In this way, users can efficiently obtain reliable information on infertility treatments and select the most suitable treatment method and medical institution.

[1734] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1735] Step 1:

[1736] Users launch the application on their smartphone or tablet and enter their symptoms, desired treatment, and location information.

[1737] Input: Symptoms, desired treatment, location information

[1738] Output: User input data

[1739] Specific operation: The user enters symptoms (e.g., polycystic ovary syndrome), desired treatment (e.g., in vitro fertilization), and location information into the application's input form, and presses the submit button.

[1740] Step 2:

[1741] The user terminal sends the input data to the server.

[1742] Input: User-entered data

[1743] Output: Request to server

[1744] Specific operation: The user device sends input data to the server as an HTTP request.

[1745] Step 3:

[1746] The server analyzes the received user-entered data and searches the database for relevant treatment information.

[1747] Input: User-entered data

[1748] Output: Treatment information

[1749] What it does: The server uses Flask to parse the received data and uses the requests library to query the database and retrieve relevant treatment information.

[1750] Step 4:

[1751] Based on the treatment information obtained by the server, the system will suggest the most appropriate treatment for the user's symptoms and desired treatment.

[1752] Input: Treatment information, user-entered data

[1753] Output: Optimal treatment proposal

[1754] Specific operation: The server analyzes the treatment information it has acquired, and selects and proposes the treatment that best suits the user's symptoms and wishes.

[1755] Step 5:

[1756] The server searches for the most suitable medical institution based on the user's location and desired treatment.

[1757] Input: Location information, desired treatment

[1758] Output: Medical institution information

[1759] Specific operation: The server searches the database based on the location information and desired treatment, and obtains information on the most suitable medical institution.

[1760] Step 6:

[1761] The server sends optimal treatment suggestions and medical institution information to the user's terminal.

[1762] Input: Optimal treatment proposals, medical institution information

[1763] Output: Response to user terminal

[1764] Specific operation: The server sends the optimal treatment proposal and medical institution information to the user's terminal as an HTTP response.

[1765] Step 7:

[1766] The user device receives the response from the server and displays it to the user.

[1767] Input: Response from the server

[1768] Output: Information displayed to the user

[1769] Specific operation: The user device receives the response from the server and displays the optimal treatment suggestions and medical institution information on the application interface.

[1770] In this way, users can efficiently obtain reliable information on infertility treatments and select the most suitable treatment method and medical institution.

[1771] Example 2

[1772] 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."

[1773] Conventional infertility treatment information systems have made it difficult for users to quickly and accurately obtain the information they need. They also lacked the means to provide detailed information on specific infertility treatment methods, making it difficult to respond appropriately to user inquiries. Furthermore, the user interface was inadequate, and the environment for users to easily obtain information was not provided.

[1774] 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.

[1775] In this invention, the server includes a means for a user to input a query from a terminal, a means for the terminal to send the query to the server, a means for the server to analyze the query, a means for the server to access a database to acquire information, a means for the server to send the acquired information to the terminal, and a means for the terminal to display the information to the user, thereby enabling the user to quickly and accurately acquire the necessary infertility treatment information.

[1776] "User" means any person or entity that uses the System to obtain information.

[1777] A "terminal" is an electronic device used by a user to enter inquiries and receive information.

[1778] A "server" is a computer system that receives inquiries from users, analyzes them, accesses a database to obtain information, and sends it to a terminal.

[1779] An "inquiry" is a question or request that a user inputs to the system through a terminal.

[1780] A "database" is a data storage system for storing information including past treatment results, statistical information, and information disclosures at research institutions.

[1781] "Means for obtaining information" refers to the process by which the server accesses the database and searches for and obtains the required information.

[1782] The "means for displaying information" is a function that allows the terminal to display information received from the server in a format that is easy for the user to view.

[1783] "Specific infertility treatment method" refers to a specific infertility treatment method such as PGTA or two-stage transplantation.

[1784] "Chat format" is an interface format that allows a user to make inquiries and receive responses from the system in an interactive manner.

[1785] This invention is a system that allows users to quickly and accurately obtain information about infertility treatment using a terminal. The system operates by having the user input a query from the terminal and send the query to a server. The server analyzes the query, accesses a database to obtain the necessary information, and sends the obtained information to the terminal. The terminal then displays the received information to the user.

[1786] The system uses the following hardware and software:

[1787] Devices: Electronic devices such as smartphones, tablets, and computers

[1788] Server: a high-performance computer system

[1789] Database Management System (DBMS): MySQL

[1790] Server software:Apache

[1791] Programming language: Python

[1792] As a specific example of how it works, a user enters "What is the success rate of PGTA?" into the input field of the terminal. This query is sent from the terminal to the server, and the server accesses the MySQL database to obtain information about the success rate of PGTA. The server then sends the obtained information to the terminal, and the terminal displays to the user, "The success rate of PGTA is 60%."

[1793] Example prompt sentence:

[1794] User: What is the success rate of PGTA?

[1795] Server: Retrieving information from database...

[1796] Server: PGTA has a success rate of about 60%.

[1797] This system allows users to quickly and accurately obtain detailed information about specific infertility treatment methods. It also has a chat-style inquiry response function, allowing users to obtain information in an interactive format, greatly improving user convenience.

[1798] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1799] Step 1:

[1800] The user inputs a query from the terminal.

[1801] Specifically, the user opens a browser on their smartphone or computer and enters "What is the success rate of PGTA" in the search bar. The entered query is displayed in the input field on the device.

[1802] Input: A query entered by the user (e.g., "What is the success rate of PGTA?")

[1803] Output: The query displayed in the terminal input field

[1804] Step 2:

[1805] The terminal sends a query to the server.

[1806] The device sends the query entered by the user to the server as an HTTP request, in JSON format.

[1807] Input: A query entered by the user (e.g., "What is the success rate of PGTA?")

[1808] Output: HTTP request sent to the server (e.g., {"query": "What is the success rate of PGTA?"})

[1809] Step 3:

[1810] The server parses the query.

[1811] The server analyzes the received HTTP request and extracts the query. For example, it recognizes that the query is "What is the success rate of PGTA?"

[1812] Input: HTTP request sent from the device (e.g., {"query": "What is the success rate of PGTA?"})

[1813] Output: Parsed query (e.g. "What is the success rate of PGTA?")

[1814] Step 4:

[1815] The server accesses the database to retrieve the information.

[1816] The server sends a query to the database based on the request. For example, it executes the query "SELECT success_rate FROM treatments WHERE name='PGTA'". The database returns a success rate of "60%".

[1817] Input: Parsed query (e.g., "What is the success rate of PGTA?")

[1818] Output: Information retrieved from the database (e.g. "60%")

[1819] Step 5:

[1820] The server transmits the acquired information to the terminal.

[1821] The server converts the information retrieved from the database into JSON format and sends it to the terminal as an HTTP response. For example, it sends information such as "The success rate of PGTA is 60%."

[1822] Input: Information obtained from the database (e.g. "60%")

[1823] Output: HTTP response sent to the device (e.g., {"response": "PGTA success rate is 60%"})

[1824] Step 6:

[1825] The terminal displays the information to the user.

[1826] The terminal analyzes the information received from the server and displays it in a format that is easy for the user to read. For example, it displays on the screen, "The success rate of PGTA is 60%."

[1827] Input: HTTP response sent from the server (e.g., {"response": "PGTA success rate is 60%"})

[1828] Output: Information displayed to the user (e.g., "PGTA has a 60% success rate")

[1829] (Application example 2)

[1830] 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."

[1831] Conventional infertility treatment information systems are specialized in providing information about infertility treatment, so users have had the problem of not being able to obtain information about security. Also, a separate system is required to provide security information, which reduces convenience for users.

[1832] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information about infertility treatment based on information acquired from the database, a means for responding to inquiries from users based on the information acquired from the database, a means for referencing a database for providing security information, and a means for responding to users based on the security information acquired from the database. This makes it possible to provide both infertility treatment information and security information using a single system.

[1833] "Infertility treatment information" refers to any information related to infertility treatment, including past treatment results, statistical information, and information disclosed by research institutions.

[1834] A "database" is a collection of specific information that is systematically organized and stored so that it can be searched and retrieved as needed.

[1835] A "user inquiry" is a question or request made by a user of the system seeking specific information.

[1836] "Means for responding" refers to a method or device for providing appropriate information in response to a user's inquiry.

[1837] "Security information" refers to the latest security techniques and countermeasures for phishing scams, unauthorized access, etc.

[1838] "Chat format" is a method of exchanging information in a text-based interactive format in real time.

[1839] The system for implementing this invention consists of a server, a user terminal, and a database. The server references a database containing past treatment results, statistical information, and information disclosure from research institutions, and provides appropriate information in response to user inquiries. It also references a database for providing security information, and responds to users with the latest security information.

[1840] The server uses the Flask framework to build a web application and manages information using an SQLite database. User devices, such as smartphones and PCs, access the server via the Internet. When a user requests specific information, the server retrieves the relevant information from the database and provides it to the user.

[1841] Specifically, when a user makes a query from their device such as "What are the latest phishing scams?", the server retrieves information about the latest phishing scams from the SQLite database and returns that information to the user, allowing the user to quickly obtain the information they need.

[1842] The hardware used is a computer as the server and a smartphone or PC as the user terminal. The software used is Flask (a Python framework) and SQLite (a database).

[1843] As a concrete example, if a user asks, "What are the latest phishing scams?" the server retrieves information about the latest phishing scams from the database and responds as follows:

[1844] "The latest phishing scam is using fake bank emails to steal personal information."

[1845] An example of a prompt for the generative AI model is as follows:

[1846] When a user asks, "What are the latest phishing scams?", retrieve information about the latest phishing scams from your database and respond with the following:

[1847] "The latest phishing scam is using fake bank emails to steal personal information."

[1848] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1849] Step 1:

[1850] The user inputs a query from a device. The user uses a smartphone or computer to input a question that requests specific information. For example, they might input, "What are the latest phishing scams?" The input data is a text query.

[1851] Step 2:

[1852] The terminal sends a query to the server. The query entered by the user is sent to the server via the Internet. The input data is the user's query text, and the output data is the query sent to the server.

[1853] Step 3:

[1854] The server receives the query and accesses the database. The server receives the query and accesses the SQLite database using the Flask framework. The input data is the user's query text and the output data is the result of executing the database query.

[1855] Step 4:

[1856] The server retrieves the relevant information from the database. The server executes an SQL query to retrieve information from the database that corresponds to the user's query. For example, information on "the latest phishing scam techniques." The input data is the SQL query, and the output data is the retrieved information.

[1857] Step 5:

[1858] The server generates a response based on the information it has obtained. The server generates a response to the user based on the information it has obtained. For example, it generates a response such as, "The latest phishing scam is to use fake bank emails to steal personal information." The input data is the obtained information, and the output data is the generated response text.

[1859] Step 6:

[1860] The server generates a response and sends it to the terminal. The server generates a response and sends it to the user's terminal. The input data is the generated response text, and the output data is the response sent to the terminal.

[1861] Step 7:

[1862] The device receives the response from the server and displays it to the user. The user's device displays the response received from the server. For example, it displays "The latest phishing scam uses fake bank emails to steal personal information." The input data is the response text from the server, and the output data is the response displayed to the user.

[1863] Example 3

[1864] 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."

[1865] Conventional infertility treatment information systems faced the challenge of being unable to respond quickly and appropriately to user inquiries. Furthermore, they lacked the means to provide detailed information on specific medical procedures, making it difficult for users to obtain the information they needed in a timely manner. Furthermore, the lack of a chat-style inquiry response function resulted in a poor user experience.

[1866] 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.

[1867] In this invention, the server includes means for receiving a user's inquiry, means for analyzing the inquiry, means for retrieving information from a database based on the analysis results, means for sending a prompt to the generative AI model based on the retrieved information, and means for returning a response from the generative AI model to the user. This enables prompt and appropriate responses to user inquiries. Furthermore, by including means for providing detailed information about specific medical procedures, the user can obtain the information they need in a timely manner. Furthermore, by including a chat-style inquiry response function, the user experience can be improved.

[1868] "Means for receiving inquiries from users" refers to the function that enables the server to receive inquiries sent by users via web browsers or mobile apps.

[1869] The "means for analyzing the inquiry" is a function for analyzing the content of the received inquiry and extracting keywords and intentions.

[1870] "Means for obtaining information from a database" refers to a function for obtaining necessary information from a database based on the analysis results.

[1871] "Means for sending prompt sentences to the generative AI model" is a function for sending prompt sentences generated based on the acquired information to the generative AI model.

[1872] "Means for returning a response from the generative AI model to the user" is a function for returning a response received from the generative AI model to the user.

[1873] The "means for providing information about a specific medical procedure" is a function for providing users with detailed information about a specific medical procedure.

[1874] "Means for responding to user inquiries in chat format" is a function for responding to user inquiries in chat format in real time.

[1875] MODE FOR CARRYING OUT THE INVENTION

[1876] The present invention is a system for quickly and appropriately responding to inquiries from users. A specific embodiment of this system will be described below.

[1877] 1. Generating the system program

[1878] The system program is developed using Python and runs as a web server using the Flask framework. It uses MySQL as the database and a common generative AI model (e.g., GPT-3) as the generative AI model.

[1879] 2. Program Processing

[1880] The server receives a query from the user and analyzes it. For analysis, it uses a text analysis library (e.g., NLTK or spaCy). Based on the analysis results, the server retrieves the necessary information from the database. Based on the retrieved information, the server sends a prompt to the generative AI model and returns the response from the generative AI model to the user.

[1881] 3. Specific Examples

[1882] Consider a case where a user uses a web browser or a mobile app to send a query such as "What are the advantages and disadvantages of two-stage porting?" In this case, the server processes the query as follows:

[1883] 1. A user sends a chat inquiry asking, "What are the advantages and disadvantages of two-stage transplant?"

[1884] 2. The server receives and analyzes this query.

[1885] 3. The server runs an SQL query to retrieve information about the "advantages and disadvantages of two-stage migration" from the MySQL database.

[1886] 4. Based on the information obtained, the following prompt sentence is input into the generative AI model:

[1887] Example prompt: "What are the advantages and disadvantages of a two-stage transplant? The advantages are ____ and the disadvantages are ____."

[1888] 5. A generative AI model (e.g., GPT-3) generates a response based on this prompt.

[1889] 6. The server generates a response and returns it to the user in real time.

[1890] In this way, the system can respond quickly and appropriately to inquiries from users. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1891] Step 1:

[1892] A user submits a query

[1893] A user sends a query in chat format using a web browser or a mobile app. For example, they type "What are the advantages and disadvantages of two-stage porting?" and click the send button. The input is the user's query, and the output is an HTTP request to the server.

[1894] Step 2:

[1895] The server receives the query

[1896] The server uses the Flask framework to receive an HTTP request from a user. The received request contains the user's query. The input is the HTTP request from the user, and the output is the text data of the query.

[1897] Step 3:

[1898] The server parses the query

[1899] The server analyzes the received query using a text analysis library (e.g., NLTK or spaCy). Through the analysis, keywords and intent are extracted. The input is the text data of the query, and the output is the analysis results (keywords and intent).

[1900] Step 4:

[1901] The server retrieves information from the database

[1902] The server retrieves the necessary information from the MySQL database based on the analysis results. For example, to retrieve information about "the advantages and disadvantages of two-stage migration," it executes an SQL query. The input is the analysis results, and the output is the information retrieved from the database.

[1903] Step 5:

[1904] The server sends a prompt to the generative AI model

[1905] The server generates a prompt based on the acquired information and sends it to the generative AI model. For example, it generates a prompt such as, "Please tell me about the advantages and disadvantages of two-stage transplantation. The advantages are XX and the disadvantages are △△." The input is information acquired from the database, and the output is a prompt sent to the generative AI model.

[1906] Step 6:

[1907] A generative AI model generates a response

[1908] The generative AI model generates a response based on the received prompt. For example, it might generate a response like, "The advantages of two-stage transplantation are a high success rate and a low burden on the patient. On the other hand, the disadvantages are that the surgery is complicated and time-consuming." The input is the prompt, and the output is the generated response.

[1909] Step 7:

[1910] The server returns the response to the user

[1911] The server returns the response received from the generative AI model to the user. Specifically, it returns the response as an HTTP response and displays it on the user's screen. The input is the generated response, and the output is the HTTP response to the user.

[1912] (Application example 3)

[1913] 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."

[1914] Conventional infertility treatment information systems have had the problem of being unable to respond quickly and accurately to user inquiries. Furthermore, there was a lack of detailed information on specific infertility treatment methods, which meant it took a long time for users to obtain the information they needed. Furthermore, there was a lack of technology to generate appropriate responses to user questions.

[1915] 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.

[1916] In this invention, the server includes a means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, a means for providing information on infertility treatment based on information obtained from the database, a means for responding to inquiries from users based on the information obtained from the database, and a means for generating appropriate responses to user questions using a generative AI model. This allows users to receive prompt and accurate responses, and detailed information on specific infertility treatment methods is also provided, enabling them to quickly obtain the information they need.

[1917] "Past treatment results" refers to data on the results and progress of previous infertility treatments.

[1918] "Statistical information" refers to data that shows the results of compiling and analyzing multiple treatment data.

[1919] "Information disclosure by research institutions" refers to research results and reports on infertility treatment that are made public by academic institutions and medical institutions.

[1920] A "database" is a system that systematically manages and stores data such as past treatment results, statistical information, and information disclosure by research institutions.

[1921] "Information on infertility treatment" refers to detailed data on infertility treatment methods, effects, side effects, success rates, etc.

[1922] "User inquiries" are actions by individuals using the system to ask questions or request information about infertility treatment.

[1923] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate appropriate responses to user questions.

[1924] An "appropriate response" is an answer that provides accurate and useful information to the user's question.

[1925] A system for carrying out this invention is configured as follows: The server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, and means for generating appropriate responses to user questions using a generative AI model.

[1926] Hardware and software used

[1927] Hardware: Servers, smartphones

[1928] Software: Python, SQLite, OpenAI API

[1929] Data processing and calculation

[1930] The server first connects to a database using SQLite, which contains past treatment results, statistical information, and information disclosure by research institutions. When a user accesses the system using a smartphone and makes an inquiry via chat, the server analyzes the content of the inquiry.

[1931] Retrieving information from a database

[1932] If the user's query is about a specific fertility treatment method, the server retrieves the relevant information from the database and provides it to the user. For example, if the user asks, "What are the advantages and disadvantages of two-stage transplantation?", the server retrieves information about two-stage transplantation from the database and generates a response based on that information.

[1933] Using generative AI models

[1934] If the user's query is not directly related to the database, the server uses a generative AI model to generate an appropriate response. Specifically, it uses the OpenAI API to generate a prompt for the user's question, and the AI ​​generates a response based on that prompt.

[1935] Specific examples

[1936] For example, if a user asks, "What are the specs of the iPhone 13?", the server retrieves the specs of the iPhone 13 from the database and returns them to the user. Also, if a user asks, "What are the advantages and disadvantages of two-stage porting?", the server sends the following prompt to the generative AI model:

[1937] User Question: What are the advantages and disadvantages of a two-stage transplant?

[1938] answer:

[1939] Based on this prompt, the generative AI model generates an appropriate response and provides it to the user, allowing them to obtain information quickly and accurately.

[1940] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1941] Step 1:

[1942] Users access the system using their smartphones and make inquiries via chat.

[1943] Input: User question (e.g., "What are the advantages and disadvantages of two-stage transplantation?")

[1944] Output: The user's question is sent to the server.

[1945] Specific actions: A user opens a chat application on their smartphone, types a question, and presses the send button.

[1946] Step 2:

[1947] The server receives the user's question and analyzes its contents.

[1948] Input: User's question

[1949] Output: Analysis result of question (e.g. "Question about two-stage transplant")

[1950] Specific operation: The server analyzes the received text data using a natural language processing algorithm to identify the intent of the question.

[1951] Step 3:

[1952] The server connects to the database and looks up the appropriate information.

[1953] Input: Analysis result of question content

[1954] Output: Information retrieved from the database (e.g., "Information about the advantages and disadvantages of two-stage transplantation")

[1955] What happens next: The server queries the SQLite database and retrieves the relevant information.

[1956] Step 4:

[1957] The server generates a response based on information retrieved from the database.

[1958] Input: Information retrieved from the database

[1959] Output: Response to the user (e.g., "The advantages of two-stage porting are..., the disadvantages are...")

[1960] Specific behavior: The server formats the information it obtains into text and generates a response to send to the user.

[1961] Step 5:

[1962] The server uses the generative AI model to generate appropriate responses to the user's questions.

[1963] Input: User's question

[1964] Output: The response provided by the generative AI model (e.g., "The advantages and disadvantages of two-stage transplantation are...")

[1965] Specific behavior: The server sends a prompt to the OpenAI API and receives the generated response.

[1966] Step 6:

[1967] The server generates a response and sends it to the user.

[1968] Input: Generated response content

[1969] Output: A response message that will be displayed on the user's smartphone.

[1970] Specific behavior: The server sends the generated response to the user through the chat application, which displays it on the user's smartphone.

[1971] 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.

[1972] "Example 1"

[1973] One embodiment of the present invention is an infertility treatment information provision system that incorporates an emotion engine that recognizes the user's emotions. In this system, when a user makes an inquiry to the system, the emotion engine recognizes the user's emotions. The emotion engine analyzes emotions from the user's text input or voice input, and the results are used by the system. For example, if the user expresses anxiety, the system recognizes that emotion and provides information that will alleviate the anxiety.

[1974] "Example 2"

[1975] Another embodiment of the present invention is a system in which an emotion engine provides information about infertility treatment based on a user's emotions. In this system, the emotion engine analyzes the user's emotions and provides information appropriate to those emotions. For example, if the user expresses hopeful emotions, the system provides information about success stories and new treatments.

[1976] "Example 3"

[1977] In another embodiment of the present invention, an emotion engine responds to a user's inquiry based on the user's emotion. In this system, the emotion engine analyzes the user's emotion and generates a response appropriate to the emotion. For example, if the user is depressed, the system generates a response that includes words of encouragement.

[1978] The processing flow of each embodiment will be described below.

[1979] "Example 1"

[1980] Step 1: The user queries the system.

[1981] Step 2: The emotion engine analyzes emotions from the user's text or voice input.

[1982] Step 3: The system uses the analysis results of the emotion engine to provide information that matches the user's emotions.

[1983] "Example 2"

[1984] Step 1: The emotion engine analyzes the user's emotions.

[1985] Step 2: The system uses the analysis results of the emotion engine to provide information that matches the user's emotions.

[1986] "Example 3"

[1987] Step 1: The emotion engine analyzes the user's emotions.

[1988] Step 2: The system uses the analysis results of the emotion engine to generate a response that matches the user's emotions.

[1989] Example 1

[1990] 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."

[1991] Conventional infertility treatment information systems provide information without considering the user's feelings, and therefore are unable to fully alleviate the anxiety and stress felt by users. In addition, the lack of appropriate information based on the user's feelings has led to a problem of reduced user satisfaction.

[1992] 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.

[1993] In this invention, the server includes means for referencing a database containing past treatment results, statistical information, and information disclosure by research institutions, means for providing information on infertility treatment based on information obtained from the database, means for responding to inquiries from users based on the information obtained from the database, emotion analysis means for recognizing the user's emotions, and means for providing appropriate information based on the emotions recognized by the emotion analysis means. This makes it possible to provide appropriate information according to the user's emotions, reducing user anxiety and stress and improving satisfaction.

[1994] "Past treatment results" refers to data on the results and process of past infertility treatments.

[1995] "Statistical information" is the result of compiling and analyzing data related to infertility treatment.

[1996] "Information disclosure by research institutions" refers to research results and reports on infertility treatment that are made public by research institutions.

[1997] A "database" is a system that organizes and stores information including past treatment results, statistical information, and information disclosure by research institutions.

[1998] "Emotion analysis means" is a technology that analyzes emotions from a user's text or voice input.

[1999] "Means for providing appropriate information" refers to technology that selects and provides information that is useful to the user based on the results of analyzing the user's emotions.

[2000] "Means for responding to inquiries" refers to technology that provides answers to questions and requests from users based on information retrieved from a database.

[2001] The present invention is a system for providing information on infertility treatment, which provides appropriate information to users by referencing a database containing past treatment results, statistical information, and information disclosure by research institutions. Furthermore, it has the function of recognizing the user's emotions and providing information based on those emotions.

[2002] Hardware and software used

[2003] 1. Server

[2004] The server uses a database management system (e.g., MySQL, PostgreSQL) to manage data including past treatment results, statistical information, and information disclosure at research institutions.

[2005] The server uses web server software (e.g., Apache, Nginx) to receive and process HTTP requests from users.

[2006] 2. Emotion analysis method

[2007] As a means of sentiment analysis, we use natural language processing APIs (e.g., Google Cloud Natural Language API, IBM Watson Tone Analyzer) to analyze emotions from user text and voice input.

[2008] 3. Terminal

[2009] Users access the system using a web browser (e.g., Google Chrome, Safari) or a mobile app.

[2010] Data processing and calculation

[2011] 1. Database Reference

[2012] The server uses a database management system to access data including past treatment results, statistical information, and information disclosures from research institutions, thereby obtaining basic data to provide appropriate information in response to user inquiries.

[2013] 2. Emotion analysis

[2014] The server sends the user's text or voice input to the emotion analysis means, which analyzes the user's emotions. The analysis results are returned to the server in JSON format.

[2015] 3. Information provision

[2016] The server searches for appropriate information from the database based on the emotion analysis results and provides it to the user. For example, if the user expresses anxiety, it provides information on success stories and relaxation methods to alleviate anxiety.

[2017] Specific examples

[2018] For example, if a user enters the text "I'm worried because my recent treatment hasn't gone well," the process would proceed as follows:

[2019] 1. A user accesses the system using Google Chrome.

[2020] 2. The user types "I'm worried because my recent treatment wasn't going well" int...

Claims

1. A system for providing infertility treatment information, A means for accessing a database including at least one of past treatment results, statistical information, and information disclosure at research institutions; A means for providing information on infertility treatment based on information acquired from the database; means for retrieving information from said database in response to a user inquiry; A means for sending a prompt sentence to a generative AI model based on the acquired information; and means for returning a response from the generative AI model to the user. system.

2. The sending means sends a prompt including at least one of advantages, disadvantages, and a success rate of a specific infertility treatment method. The system of claim 1 .

3. The generative AI model recognizes the user's emotion using the prompt sentence, and outputs information corresponding to the emotion as the response. The system of claim 1 .

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